Expectation maximization algorithm over Fourier series (EMoFS)
The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior information on parametrized probability distribution function (pdf) and its family. For the highly dynamic environments, however, there is a high...
Saved in:
| Published in | Signal processing Vol. 194; p. 108453 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2022
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior information on parametrized probability distribution function (pdf) and its family. For the highly dynamic environments, however, there is a high potential of mismatch between the domain characteristics and the pre-assumed distribution. The observed data may have been drawn based on an unknown pdf, which can additionally be combination of discontinuous functions with different types. For such cases, it is very likely that an arbitrary selection of a mixture distribution would yield worse performance. Even if the domain characteristics are captured correctly, i.e. the pdf family is known, another complexity may arise due to the fact that a tractable and a closed form cannot always be obtained. Addressing these two problems, we present the EM over Fourier Series (EMoFS) approach for univariate problems to be solved with EM. Our solution produces the true pdf approximately; thus sidesteps the necessity of a prior assumption. Additionally it guarantees a tractable and closed form for E-step. We verify and evaluate our model via comparison with state of the art solutions, theoretical experiments and real world problems. |
|---|---|
| AbstractList | The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior information on parametrized probability distribution function (pdf) and its family. For the highly dynamic environments, however, there is a high potential of mismatch between the domain characteristics and the pre-assumed distribution. The observed data may have been drawn based on an unknown pdf, which can additionally be combination of discontinuous functions with different types. For such cases, it is very likely that an arbitrary selection of a mixture distribution would yield worse performance. Even if the domain characteristics are captured correctly, i.e. the pdf family is known, another complexity may arise due to the fact that a tractable and a closed form cannot always be obtained. Addressing these two problems, we present the EM over Fourier Series (EMoFS) approach for univariate problems to be solved with EM. Our solution produces the true pdf approximately; thus sidesteps the necessity of a prior assumption. Additionally it guarantees a tractable and closed form for E-step. We verify and evaluate our model via comparison with state of the art solutions, theoretical experiments and real world problems. |
| ArticleNumber | 108453 |
| Author | Yildiz, Mürsel |
| Author_xml | – sequence: 1 givenname: Mürsel surname: Yildiz fullname: Yildiz, Mürsel email: muersel.yildiz@continental-corporation.com organization: Continental AG, Germany |
| BookMark | eNqFkLFOwzAQhj0UibbwBgwZYUixG9txGCqhKgWkIgZgthz7Uhw1cWWHqvTpcRUmBph-3Z2-X_pugkad6wChK4JnBBN-28yC3ey8m83xnMSVoCwboXE8sZRwQc_RJIQGY0wyjsdoUR52oHvVW9clrTrY1h6HQW03ztv-o03cHnyycp_exgwQIyTX5bNbvd5coLNabQNc_uQUva_Kt-Vjun55eFrer1OdYd6nguVVwXCe4xyoIQVwQwGIoVjVRhijATQxhtOMaS4YcFZklNUgRFUUFYdsiujQq70LwUMtd962yn9JguXJWzZy8JYnbzl4R-zuF6bt4Np7Zbf_wYsBhii2j-4yaAudBmN9fJk0zv5d8A2IkXse |
| CitedBy_id | crossref_primary_10_1016_j_specom_2023_06_001 crossref_primary_10_1016_j_sigpro_2025_109919 crossref_primary_10_1016_j_ymssp_2023_110113 |
| Cites_doi | 10.1016/j.atmosenv.2018.01.056 10.1109/TCOMM.2020.2975169 10.1016/j.compmedimag.2016.11.006 10.1109/TASLP.2016.2553457 10.1007/s13571-012-0055-y 10.1080/02664763.2016.1214692 10.1111/j.1365-2966.2009.15576.x 10.1162/evco.2007.15.1.1 10.1214/aos/1059655912 10.1109/78.324732 10.1111/j.1420-9101.2009.01775.x 10.1016/j.sigpro.2016.10.015 10.1109/LSP.2021.3065600 10.1109/79.543975 10.1016/j.sigpro.2016.04.014 10.1016/j.sigpro.2015.09.031 10.1109/TPAMI.2017.2717829 10.1109/TASE.2016.2624279 10.1111/j.1467-9868.2005.00499.x 10.1111/j.2517-6161.1977.tb01600.x 10.1109/TEVC.2017.2680320 10.1109/JIOT.2018.2871831 10.1016/j.csda.2015.04.011 10.1111/1467-9868.00176 10.1111/j.1467-9876.2006.00560.x 10.1109/ISIT.2005.1523402 10.1016/j.jprocont.2018.12.010 10.1111/j.1439-0388.2012.01000.x 10.1007/s11069-017-2950-z 10.1016/j.eswa.2014.09.059 10.1016/j.sigpro.2018.04.013 10.1109/TPAMI.2016.2522425 10.1371/journal.pcbi.1005896 10.1109/TFUZZ.2018.2857725 10.1561/2000000034 10.1016/j.sigpro.2017.10.012 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.sigpro.2021.108453 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | 10_1016_j_sigpro_2021_108453 S0165168421004904 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN ABBOA ABDPE ABFNM ABFRF ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEFWE AEIPS AEKER AENEX AFJKZ AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SSH SST SSV SSZ T5K TAE TN5 WUQ XPP ZMT ~02 ~G- AAYWO AAYXX ACVFH ADCNI AEUPX AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKYEP APXCP CITATION |
| ID | FETCH-LOGICAL-c306t-857b9507707e4d19e6d4ee1d40afd8ddceec1dd6435c685e659345fe88b99b6e3 |
| IEDL.DBID | .~1 |
| ISSN | 0165-1684 |
| IngestDate | Tue Jul 01 02:07:32 EDT 2025 Thu Apr 24 23:05:59 EDT 2025 Sun Apr 06 06:53:17 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Expectation maximization algorithm Fourier series Maximum likelihood |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-857b9507707e4d19e6d4ee1d40afd8ddceec1dd6435c685e659345fe88b99b6e3 |
| ParticipantIDs | crossref_primary_10_1016_j_sigpro_2021_108453 crossref_citationtrail_10_1016_j_sigpro_2021_108453 elsevier_sciencedirect_doi_10_1016_j_sigpro_2021_108453 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | May 2022 2022-05-00 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: May 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Signal processing |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Booth, Hobert (bib0039) 1999; 61 Wu (bib0052) 1983 Gramacki (bib0056) 2018 Qiu, Chen, Wang, Zhu, Wang, Qiu (bib0009) 2018; 178 Manouchehri, Bouguila (bib0014) 2018 Dempster, Laird, Rubin (bib0002) 1977 Walter, Drude, Haeb-Umbach (bib0048) 2015 Orozco-Lugo, Lara, Sandoval-Curmina, Galvan-Tejada (bib0061) 2020 F. Wu, S. Zilberstein, N.R. Jennings, Monte-carlo expectation maximization for decentralized POMDPs (2013). (2004). McLachlan, Krishnan (bib0001) 2007; vol. 382 Schwartz, Gannot, Habets (bib0021) 2016; 24 Allassonniere, Kuhn (bib0037) 2015; 91 Xu, Hsu, Maleki (bib0036) 2016 Matsuyama (bib0025) 2019 S. Borman, The Expectation Maximization Algorithm: A Short Tutorialunpublished paper available at Ge, Xie, Li, Yang (bib0004) 2016; 124 Yuan, Wu, Guo, Ng, Yuan, Hanzo (bib0005) 2020; 68 Matilainen, Mäntysaari, Lidauer, Strandén, Thompson (bib0028) 2012; 129 Chen, Morris, Martin (bib0049) 2006; 55 Shin, Sekora, Byun (bib0050) 2009; 400 J.W. Eaton, D. Bateman, S. Hauberg, R. Wehbring, GNU Octave version 5.2.0 manual: a high-level interactive language for numerical computations, 2020. Zuo, Lu, Zhang, Liu (bib0045) 2018; 27 Q. Zou, H. Zhang, H. Yang, Expectation-maximization-aided hybrid generalized expectation consistent for sparse signal reconstruction, (2021) A.B. Dieng, J. Paisley, Reweighted expectation maximization, (2019) Dellaert (bib0053) 2002 Frank (bib0055) 2009; 22 Moon (bib0034) 1996; 13 Li, Nehorai (bib0008) 2018; 150 . Gupta, Chen (bib0035) 2011; 4 Ahmed (bib0024) 2017; 44 Guo, Li, Xu, Ansari (bib0013) 2018; 6 Maitra (bib0016) 2013; 75 Chen, Zhu, Teh, Zhang (bib0030) 2018 Song, Ali, Bouguila (bib0046) 2019 Pereira, López-Valcarce, Pages-Zamora (bib0003) 2018; 144 Brookes, Busia, Fannjiang, Murphy, Listgarten (bib0023) 2020 Fessler, Hero (bib0029) 1994; 42 McDowell, Manandhar, Vockley, Schmid, Reddy, Engelhardt (bib0047) 2018; 14 Evangelidis, Horaud (bib0018) 2017; 40 Ravichandar, Dani (bib0020) 2017; 14 M. Desana, C. Schnörr, Expectation maximization for sum-product networks as exponential family mixture models, (2016) Jackson (bib0060) 2012 Tzoreff, Weiss (bib0007) 2017; 133 Matlab (bib0059) 2012 J. Dauwels, S. Korl, H.-A. Loeliger, Expectation maximization as message passing, (2005) D’Arca, Robertson, Hopgood (bib0011) 2016; 129 Karakatsanis, Tsoumpas, Zaidi (bib0010) 2017; 60 Deng, Xu, Li, He (bib0051) 2015; 42 Igel, Hansen, Roth (bib0042) 2007; 15 Sammaknejad, Zhao, Huang (bib0012) 2019; 73 K. Greff, S. Van Steenkiste, J. Schmidhuber, Neural expectation maximization, (2017) Gebru, Alameda-Pineda, Forbes, Horaud (bib0019) 2016; 38 Caffo, Jank, Jones (bib0027) 2005; 67 Forero, Cano, Giannakis (bib0040) 2008 Park, Foulds, Choudhary, Welling (bib0032) 2017 Beyer, Sendhoff (bib0043) 2017; 21 Rasmussen (bib0044) 1999; vol. 12 Pavlenko (bib0057) 2017; 89 Fort, Moulines (bib0026) 2003; 31 Farag, El-Baz, Gimel’farb (bib0041) 2004; vol. 3 Yu, Chen (bib0062) 2019 Nguyen, Forbes, McLachlan (bib0022) 2020 Rasmussen (10.1016/j.sigpro.2021.108453_bib0044) 1999; vol. 12 10.1016/j.sigpro.2021.108453_bib0006 Qiu (10.1016/j.sigpro.2021.108453_bib0009) 2018; 178 Fort (10.1016/j.sigpro.2021.108453_bib0026) 2003; 31 Caffo (10.1016/j.sigpro.2021.108453_bib0027) 2005; 67 Park (10.1016/j.sigpro.2021.108453_bib0032) 2017 Moon (10.1016/j.sigpro.2021.108453_bib0034) 1996; 13 Farag (10.1016/j.sigpro.2021.108453_bib0041) 2004; vol. 3 Sammaknejad (10.1016/j.sigpro.2021.108453_bib0012) 2019; 73 Orozco-Lugo (10.1016/j.sigpro.2021.108453_bib0061) 2020 Manouchehri (10.1016/j.sigpro.2021.108453_bib0014) 2018 Frank (10.1016/j.sigpro.2021.108453_bib0055) 2009; 22 McLachlan (10.1016/j.sigpro.2021.108453_bib0001) 2007; vol. 382 Ahmed (10.1016/j.sigpro.2021.108453_bib0024) 2017; 44 Matsuyama (10.1016/j.sigpro.2021.108453_bib0025) 2019 Nguyen (10.1016/j.sigpro.2021.108453_bib0022) 2020 Dempster (10.1016/j.sigpro.2021.108453_bib0002) 1977 Maitra (10.1016/j.sigpro.2021.108453_bib0016) 2013; 75 Dellaert (10.1016/j.sigpro.2021.108453_sbref0053) 2002 Li (10.1016/j.sigpro.2021.108453_bib0008) 2018; 150 Brookes (10.1016/j.sigpro.2021.108453_bib0023) 2020 Gramacki (10.1016/j.sigpro.2021.108453_bib0056) 2018 10.1016/j.sigpro.2021.108453_bib0031 10.1016/j.sigpro.2021.108453_bib0033 Allassonniere (10.1016/j.sigpro.2021.108453_bib0037) 2015; 91 Gebru (10.1016/j.sigpro.2021.108453_bib0019) 2016; 38 Gupta (10.1016/j.sigpro.2021.108453_bib0035) 2011; 4 10.1016/j.sigpro.2021.108453_bib0038 Beyer (10.1016/j.sigpro.2021.108453_bib0043) 2017; 21 Deng (10.1016/j.sigpro.2021.108453_bib0051) 2015; 42 McDowell (10.1016/j.sigpro.2021.108453_bib0047) 2018; 14 Yuan (10.1016/j.sigpro.2021.108453_bib0005) 2020; 68 Chen (10.1016/j.sigpro.2021.108453_bib0030) 2018 Schwartz (10.1016/j.sigpro.2021.108453_bib0021) 2016; 24 Yu (10.1016/j.sigpro.2021.108453_bib0062) 2019 Shin (10.1016/j.sigpro.2021.108453_bib0050) 2009; 400 Matilainen (10.1016/j.sigpro.2021.108453_bib0028) 2012; 129 Tzoreff (10.1016/j.sigpro.2021.108453_bib0007) 2017; 133 Guo (10.1016/j.sigpro.2021.108453_bib0013) 2018; 6 Forero (10.1016/j.sigpro.2021.108453_bib0040) 2008 Walter (10.1016/j.sigpro.2021.108453_bib0048) 2015 Chen (10.1016/j.sigpro.2021.108453_bib0049) 2006; 55 D’Arca (10.1016/j.sigpro.2021.108453_bib0011) 2016; 129 Fessler (10.1016/j.sigpro.2021.108453_bib0029) 1994; 42 Igel (10.1016/j.sigpro.2021.108453_bib0042) 2007; 15 Song (10.1016/j.sigpro.2021.108453_bib0046) 2019 10.1016/j.sigpro.2021.108453_bib0054 Evangelidis (10.1016/j.sigpro.2021.108453_bib0018) 2017; 40 10.1016/j.sigpro.2021.108453_bib0058 Zuo (10.1016/j.sigpro.2021.108453_bib0045) 2018; 27 Karakatsanis (10.1016/j.sigpro.2021.108453_bib0010) 2017; 60 10.1016/j.sigpro.2021.108453_bib0017 10.1016/j.sigpro.2021.108453_bib0015 Matlab (10.1016/j.sigpro.2021.108453_bib0059) 2012 Wu (10.1016/j.sigpro.2021.108453_bib0052) 1983 Booth (10.1016/j.sigpro.2021.108453_bib0039) 1999; 61 Ge (10.1016/j.sigpro.2021.108453_bib0004) 2016; 124 Pereira (10.1016/j.sigpro.2021.108453_bib0003) 2018; 144 Jackson (10.1016/j.sigpro.2021.108453_bib0060) 2012 Xu (10.1016/j.sigpro.2021.108453_bib0036) 2016 Pavlenko (10.1016/j.sigpro.2021.108453_bib0057) 2017; 89 Ravichandar (10.1016/j.sigpro.2021.108453_bib0020) 2017; 14 |
| References_xml | – volume: 75 start-page: 293 year: 2013 end-page: 318 ident: bib0016 article-title: On the expectation-maximization algorithm for rice-rayleigh mixtures with application to noise parameter estimation in magnitude MR datasets publication-title: Sankhya B – year: 2012 ident: bib0059 article-title: Matlab – volume: 27 start-page: 291 year: 2018 end-page: 303 ident: bib0045 article-title: Fuzzy transfer learning using an infinite gaussian mixture model and active learning publication-title: IEEE Trans. Fuzzy Syst. – volume: 129 start-page: 137 year: 2016 end-page: 149 ident: bib0011 article-title: Robust indoor speaker recognition in a network of audio and video sensors publication-title: Signal Process. – volume: 24 start-page: 1495 year: 2016 end-page: 1510 ident: bib0021 article-title: An expectation-maximization algorithm for multimicrophone speech dereverberation and noise reduction with coherence matrix estimation publication-title: IEEE/ACM Trans. Audio Speech Lang. Process. – volume: 61 start-page: 265 year: 1999 end-page: 285 ident: bib0039 article-title: Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm publication-title: J. R. Stat. Soc. Ser. B – volume: 15 start-page: 1 year: 2007 end-page: 28 ident: bib0042 article-title: Covariance matrix adaptation for multi-objective optimization publication-title: Evol. Comput. – volume: 129 start-page: 457 year: 2012 end-page: 468 ident: bib0028 article-title: Employing a monte carlo algorithm in expectation maximization restricted maximum likelihood estimation of the linear mixed model publication-title: J. Anim. Breed. Genet. – start-page: 7 year: 2019 end-page: 42 ident: bib0062 article-title: Stability analysis of frame slotted aloha protocol publication-title: Tag Counting and Monitoring in Large-Scale RFID Systems – start-page: 1989 year: 2008 end-page: 1992 ident: bib0040 article-title: Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks publication-title: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing – volume: 6 start-page: 2573 year: 2018 end-page: 2582 ident: bib0013 article-title: Expectation maximization indoor localization utilizing supporting set for internet of things publication-title: IEEE Internet Things J. – start-page: 1 year: 2020 end-page: 18 ident: bib0022 article-title: Mini-batch learning of exponential family finite mixture models publication-title: Stat. Comput. – volume: vol. 3 start-page: 1871 year: 2004 end-page: 1874 ident: bib0041 article-title: Density estimation using modified expectation-maximization algorithm for a linear combination of Gaussians publication-title: 2004 International Conference on Image Processing, 2004. ICIP’04. – volume: 400 start-page: 1897 year: 2009 end-page: 1910 ident: bib0050 article-title: Detecting variability in massive astronomical time series data–I. Application of an infinite gaussian mixture model publication-title: Mon. Not. R. Astron. Soc. – volume: 144 start-page: 226 year: 2018 end-page: 237 ident: bib0003 article-title: Parameter estimation in wireless sensor networks with faulty transducers: a distributed EM approach publication-title: Signal Process. – volume: vol. 12 start-page: 554 year: 1999 end-page: 560 ident: bib0044 article-title: The infinite gaussian mixture model publication-title: NIPS – start-page: 7978 year: 2018 end-page: 7988 ident: bib0030 article-title: Stochastic expectation maximization with variance reduction publication-title: NeurIPS – volume: 124 start-page: 147 year: 2016 end-page: 155 ident: bib0004 article-title: Global image completion with joint sparse patch selection and optimal seam synthesis publication-title: Signal Process. – volume: 133 start-page: 32 year: 2017 end-page: 39 ident: bib0007 article-title: Expectation-maximization algorithm for direct position determination publication-title: Signal Process. – volume: 60 start-page: 11 year: 2017 end-page: 21 ident: bib0010 article-title: Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation publication-title: Comput. Med. Imaging Graph. – year: 2018 ident: bib0056 article-title: Nonparametric Kernel Density Estimation and Its Computational Aspects – year: 2002 ident: bib0053 article-title: The Expectation Maximization Algorithm – reference: J. Dauwels, S. Korl, H.-A. Loeliger, Expectation maximization as message passing, (2005) – volume: 67 start-page: 235 year: 2005 end-page: 251 ident: bib0027 article-title: Ascent-based monte carlo expectation–maximization publication-title: J. R. Stat. Soc. Ser. B – volume: 42 start-page: 1987 year: 2015 end-page: 1997 ident: bib0051 article-title: An infinite gaussian mixture model with its application in hyperspectral unmixing publication-title: Expert Syst. Appl. – start-page: 107827 year: 2020 ident: bib0061 article-title: Offered load estimation in random access multipacket perception systems using the expectation-maximization algorithm publication-title: Signal Process. – start-page: 264 year: 2019 end-page: 274 ident: bib0046 article-title: Bayesian learning of infinite asymmetric Gaussian mixture models for background subtraction publication-title: International Conference on Image Analysis and Recognition – reference: S. Borman, The Expectation Maximization Algorithm: A Short Tutorialunpublished paper available at – volume: 89 start-page: 19 year: 2017 end-page: 33 ident: bib0057 article-title: Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution publication-title: Nat. Hazards – start-page: 227 year: 2018 end-page: 232 ident: bib0014 article-title: Learning of finite two-dimensional beta mixture models publication-title: 2018 9th International Symposium on Signal, Image, Video and Communications (ISIVC) – start-page: 896 year: 2017 end-page: 904 ident: bib0032 article-title: DP-EM: differentially private expectation maximization publication-title: Artificial Intelligence and Statistics – reference: (2004). – year: 2012 ident: bib0060 article-title: Fourier Series and Orthogonal Polynomials – reference: A.B. Dieng, J. Paisley, Reweighted expectation maximization, (2019) – volume: 4 start-page: 223 year: 2011 end-page: 296 ident: bib0035 article-title: Theory and use of the EM algorithm publication-title: Foundations Trends® Signal Process. – volume: 14 start-page: e1005896 year: 2018 ident: bib0047 article-title: Clustering gene expression time series data using an infinite gaussian process mixture model publication-title: PLoS Comput. Biol. – start-page: 1 year: 1977 end-page: 38 ident: bib0002 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B – volume: 38 start-page: 2402 year: 2016 end-page: 2415 ident: bib0019 article-title: Em algorithms for weighted-data clustering with application to audio-visual scene analysis publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 55 start-page: 699 year: 2006 end-page: 715 ident: bib0049 article-title: Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring publication-title: J. R. Stat. Soc. Ser. C – volume: vol. 382 year: 2007 ident: bib0001 article-title: The EM Algorithm and Extensions – volume: 14 start-page: 855 year: 2017 end-page: 868 ident: bib0020 article-title: Human intention inference using expectation-maximization algorithm with online model learning publication-title: IEEE Trans. Autom. Sci. Eng. – reference: F. Wu, S. Zilberstein, N.R. Jennings, Monte-carlo expectation maximization for decentralized POMDPs (2013). – volume: 68 start-page: 2963 year: 2020 end-page: 2977 ident: bib0005 article-title: Iterative joint channel estimation, user activity tracking, and data detection for FTN-NOMA systems supporting random access publication-title: IEEE Trans. Commun. – start-page: 95 year: 1983 end-page: 103 ident: bib0052 article-title: On the convergence properties of the EM algorithm publication-title: Ann. Stat. – start-page: 2676 year: 2016 end-page: 2684 ident: bib0036 article-title: Global analysis of expectation maximization for mixtures of two Gaussians publication-title: Advances in Neural Information Processing Systems – volume: 150 start-page: 116 year: 2018 end-page: 121 ident: bib0008 article-title: Gaussian mixture learning via adaptive hierarchical clustering publication-title: Signal Process. – volume: 44 start-page: 1576 year: 2017 end-page: 1608 ident: bib0024 article-title: Estimation and prediction for the generalized inverted exponential distribution based on progressively first-failure-censored data with application publication-title: J. Appl. Stat. – start-page: 459 year: 2015 end-page: 463 ident: bib0048 article-title: Source counting in speech mixtures by nonparametric Bayesian estimation of an infinite gaussian mixture model publication-title: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – start-page: 727 year: 2019 end-page: 731 ident: bib0025 article-title: Divergence family attains blockchain applications via publication-title: 2019 IEEE International Symposium on Information Theory (ISIT) – volume: 178 start-page: 158 year: 2018 end-page: 163 ident: bib0009 article-title: Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization publication-title: Atmos. Environ. – reference: Q. Zou, H. Zhang, H. Yang, Expectation-maximization-aided hybrid generalized expectation consistent for sparse signal reconstruction, (2021), – volume: 42 start-page: 2664 year: 1994 end-page: 2677 ident: bib0029 article-title: Space-alternating generalized expectation-maximization algorithm publication-title: IEEE Trans. Signal Process. – volume: 22 start-page: 1563 year: 2009 end-page: 1585 ident: bib0055 article-title: The common patterns of nature publication-title: J. Evol. Biol. – volume: 73 start-page: 123 year: 2019 end-page: 136 ident: bib0012 article-title: A review of the expectation maximization algorithm in data-driven process identification publication-title: J. Process Control – reference: K. Greff, S. Van Steenkiste, J. Schmidhuber, Neural expectation maximization, (2017) – volume: 13 start-page: 47 year: 1996 end-page: 60 ident: bib0034 article-title: The expectation-maximization algorithm publication-title: IEEE Signal Process. Mag. – reference: . – volume: 91 start-page: 4 year: 2015 end-page: 19 ident: bib0037 article-title: Convergent stochastic expectation maximization algorithm with efficient sampling in high dimension. application to deformable template model estimation publication-title: Comput. Stat. Data Anal. – volume: 21 start-page: 746 year: 2017 end-page: 759 ident: bib0043 article-title: Simplify your covariance matrix adaptation evolution strategy publication-title: IEEE Trans. Evol. Comput. – start-page: 189 year: 2020 end-page: 190 ident: bib0023 article-title: A view of estimation of distribution algorithms through the lens of expectation-maximization publication-title: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion – reference: J.W. Eaton, D. Bateman, S. Hauberg, R. Wehbring, GNU Octave version 5.2.0 manual: a high-level interactive language for numerical computations, 2020. – volume: 31 start-page: 1220 year: 2003 end-page: 1259 ident: bib0026 article-title: Convergence of the monte carlo expectation maximization for curved exponential families publication-title: Ann. Stat. – reference: M. Desana, C. Schnörr, Expectation maximization for sum-product networks as exponential family mixture models, (2016) – volume: 40 start-page: 1397 year: 2017 end-page: 1410 ident: bib0018 article-title: Joint alignment of multiple point sets with batch and incremental expectation-maximization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 178 start-page: 158 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0009 article-title: Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2018.01.056 – volume: 68 start-page: 2963 issue: 5 year: 2020 ident: 10.1016/j.sigpro.2021.108453_bib0005 article-title: Iterative joint channel estimation, user activity tracking, and data detection for FTN-NOMA systems supporting random access publication-title: IEEE Trans. Commun. doi: 10.1109/TCOMM.2020.2975169 – volume: 60 start-page: 11 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0010 article-title: Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2016.11.006 – volume: 24 start-page: 1495 issue: 9 year: 2016 ident: 10.1016/j.sigpro.2021.108453_bib0021 article-title: An expectation-maximization algorithm for multimicrophone speech dereverberation and noise reduction with coherence matrix estimation publication-title: IEEE/ACM Trans. Audio Speech Lang. Process. doi: 10.1109/TASLP.2016.2553457 – start-page: 896 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0032 article-title: DP-EM: differentially private expectation maximization – volume: 75 start-page: 293 issue: 2 year: 2013 ident: 10.1016/j.sigpro.2021.108453_bib0016 article-title: On the expectation-maximization algorithm for rice-rayleigh mixtures with application to noise parameter estimation in magnitude MR datasets publication-title: Sankhya B doi: 10.1007/s13571-012-0055-y – volume: 44 start-page: 1576 issue: 9 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0024 article-title: Estimation and prediction for the generalized inverted exponential distribution based on progressively first-failure-censored data with application publication-title: J. Appl. Stat. doi: 10.1080/02664763.2016.1214692 – start-page: 189 year: 2020 ident: 10.1016/j.sigpro.2021.108453_bib0023 article-title: A view of estimation of distribution algorithms through the lens of expectation-maximization – year: 2012 ident: 10.1016/j.sigpro.2021.108453_bib0059 – start-page: 7978 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0030 article-title: Stochastic expectation maximization with variance reduction – start-page: 1989 year: 2008 ident: 10.1016/j.sigpro.2021.108453_bib0040 article-title: Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks – start-page: 107827 year: 2020 ident: 10.1016/j.sigpro.2021.108453_bib0061 article-title: Offered load estimation in random access multipacket perception systems using the expectation-maximization algorithm publication-title: Signal Process. – volume: 400 start-page: 1897 issue: 4 year: 2009 ident: 10.1016/j.sigpro.2021.108453_bib0050 article-title: Detecting variability in massive astronomical time series data–I. Application of an infinite gaussian mixture model publication-title: Mon. Not. R. Astron. Soc. doi: 10.1111/j.1365-2966.2009.15576.x – start-page: 2676 year: 2016 ident: 10.1016/j.sigpro.2021.108453_bib0036 article-title: Global analysis of expectation maximization for mixtures of two Gaussians – volume: 15 start-page: 1 issue: 1 year: 2007 ident: 10.1016/j.sigpro.2021.108453_bib0042 article-title: Covariance matrix adaptation for multi-objective optimization publication-title: Evol. Comput. doi: 10.1162/evco.2007.15.1.1 – volume: 31 start-page: 1220 issue: 4 year: 2003 ident: 10.1016/j.sigpro.2021.108453_bib0026 article-title: Convergence of the monte carlo expectation maximization for curved exponential families publication-title: Ann. Stat. doi: 10.1214/aos/1059655912 – volume: 42 start-page: 2664 issue: 10 year: 1994 ident: 10.1016/j.sigpro.2021.108453_bib0029 article-title: Space-alternating generalized expectation-maximization algorithm publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.324732 – start-page: 227 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0014 article-title: Learning of finite two-dimensional beta mixture models – volume: 22 start-page: 1563 issue: 8 year: 2009 ident: 10.1016/j.sigpro.2021.108453_bib0055 article-title: The common patterns of nature publication-title: J. Evol. Biol. doi: 10.1111/j.1420-9101.2009.01775.x – volume: 133 start-page: 32 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0007 article-title: Expectation-maximization algorithm for direct position determination publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.10.015 – start-page: 727 year: 2019 ident: 10.1016/j.sigpro.2021.108453_bib0025 article-title: Divergence family attains blockchain applications via α-EM algorithm – year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0056 – ident: 10.1016/j.sigpro.2021.108453_bib0017 doi: 10.1109/LSP.2021.3065600 – volume: 13 start-page: 47 issue: 6 year: 1996 ident: 10.1016/j.sigpro.2021.108453_bib0034 article-title: The expectation-maximization algorithm publication-title: IEEE Signal Process. Mag. doi: 10.1109/79.543975 – ident: 10.1016/j.sigpro.2021.108453_bib0058 – volume: 129 start-page: 137 year: 2016 ident: 10.1016/j.sigpro.2021.108453_bib0011 article-title: Robust indoor speaker recognition in a network of audio and video sensors publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.04.014 – volume: 124 start-page: 147 year: 2016 ident: 10.1016/j.sigpro.2021.108453_bib0004 article-title: Global image completion with joint sparse patch selection and optimal seam synthesis publication-title: Signal Process. doi: 10.1016/j.sigpro.2015.09.031 – start-page: 7 year: 2019 ident: 10.1016/j.sigpro.2021.108453_bib0062 article-title: Stability analysis of frame slotted aloha protocol – ident: 10.1016/j.sigpro.2021.108453_bib0054 – year: 2012 ident: 10.1016/j.sigpro.2021.108453_bib0060 – volume: 40 start-page: 1397 issue: 6 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0018 article-title: Joint alignment of multiple point sets with batch and incremental expectation-maximization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2717829 – ident: 10.1016/j.sigpro.2021.108453_bib0033 – volume: vol. 382 year: 2007 ident: 10.1016/j.sigpro.2021.108453_bib0001 – start-page: 95 year: 1983 ident: 10.1016/j.sigpro.2021.108453_bib0052 article-title: On the convergence properties of the EM algorithm publication-title: Ann. Stat. – volume: 14 start-page: 855 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0020 article-title: Human intention inference using expectation-maximization algorithm with online model learning publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2624279 – volume: 67 start-page: 235 issue: 2 year: 2005 ident: 10.1016/j.sigpro.2021.108453_bib0027 article-title: Ascent-based monte carlo expectation–maximization publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/j.1467-9868.2005.00499.x – start-page: 1 year: 1977 ident: 10.1016/j.sigpro.2021.108453_bib0002 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume: 21 start-page: 746 issue: 5 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0043 article-title: Simplify your covariance matrix adaptation evolution strategy publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2680320 – volume: 6 start-page: 2573 issue: 2 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0013 article-title: Expectation maximization indoor localization utilizing supporting set for internet of things publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2871831 – volume: 91 start-page: 4 year: 2015 ident: 10.1016/j.sigpro.2021.108453_bib0037 article-title: Convergent stochastic expectation maximization algorithm with efficient sampling in high dimension. application to deformable template model estimation publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2015.04.011 – volume: 61 start-page: 265 issue: 1 year: 1999 ident: 10.1016/j.sigpro.2021.108453_bib0039 article-title: Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/1467-9868.00176 – start-page: 264 year: 2019 ident: 10.1016/j.sigpro.2021.108453_bib0046 article-title: Bayesian learning of infinite asymmetric Gaussian mixture models for background subtraction – volume: 55 start-page: 699 issue: 5 year: 2006 ident: 10.1016/j.sigpro.2021.108453_bib0049 article-title: Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring publication-title: J. R. Stat. Soc. Ser. C doi: 10.1111/j.1467-9876.2006.00560.x – ident: 10.1016/j.sigpro.2021.108453_bib0006 doi: 10.1109/ISIT.2005.1523402 – volume: 73 start-page: 123 year: 2019 ident: 10.1016/j.sigpro.2021.108453_bib0012 article-title: A review of the expectation maximization algorithm in data-driven process identification publication-title: J. Process Control doi: 10.1016/j.jprocont.2018.12.010 – volume: 129 start-page: 457 issue: 6 year: 2012 ident: 10.1016/j.sigpro.2021.108453_bib0028 article-title: Employing a monte carlo algorithm in expectation maximization restricted maximum likelihood estimation of the linear mixed model publication-title: J. Anim. Breed. Genet. doi: 10.1111/j.1439-0388.2012.01000.x – ident: 10.1016/j.sigpro.2021.108453_bib0038 – start-page: 459 year: 2015 ident: 10.1016/j.sigpro.2021.108453_bib0048 article-title: Source counting in speech mixtures by nonparametric Bayesian estimation of an infinite gaussian mixture model – volume: 89 start-page: 19 issue: 1 year: 2017 ident: 10.1016/j.sigpro.2021.108453_bib0057 article-title: Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution publication-title: Nat. Hazards doi: 10.1007/s11069-017-2950-z – volume: vol. 12 start-page: 554 year: 1999 ident: 10.1016/j.sigpro.2021.108453_bib0044 article-title: The infinite gaussian mixture model – volume: 42 start-page: 1987 issue: 4 year: 2015 ident: 10.1016/j.sigpro.2021.108453_bib0051 article-title: An infinite gaussian mixture model with its application in hyperspectral unmixing publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.09.059 – ident: 10.1016/j.sigpro.2021.108453_bib0015 – volume: 150 start-page: 116 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0008 article-title: Gaussian mixture learning via adaptive hierarchical clustering publication-title: Signal Process. doi: 10.1016/j.sigpro.2018.04.013 – volume: 38 start-page: 2402 issue: 12 year: 2016 ident: 10.1016/j.sigpro.2021.108453_bib0019 article-title: Em algorithms for weighted-data clustering with application to audio-visual scene analysis publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2522425 – volume: 14 start-page: e1005896 issue: 1 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0047 article-title: Clustering gene expression time series data using an infinite gaussian process mixture model publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1005896 – year: 2002 ident: 10.1016/j.sigpro.2021.108453_sbref0053 – start-page: 1 year: 2020 ident: 10.1016/j.sigpro.2021.108453_bib0022 article-title: Mini-batch learning of exponential family finite mixture models publication-title: Stat. Comput. – volume: 27 start-page: 291 issue: 2 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0045 article-title: Fuzzy transfer learning using an infinite gaussian mixture model and active learning publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2018.2857725 – volume: 4 start-page: 223 issue: 3 year: 2011 ident: 10.1016/j.sigpro.2021.108453_bib0035 article-title: Theory and use of the EM algorithm publication-title: Foundations Trends® Signal Process. doi: 10.1561/2000000034 – ident: 10.1016/j.sigpro.2021.108453_bib0031 – volume: vol. 3 start-page: 1871 year: 2004 ident: 10.1016/j.sigpro.2021.108453_bib0041 article-title: Density estimation using modified expectation-maximization algorithm for a linear combination of Gaussians – volume: 144 start-page: 226 year: 2018 ident: 10.1016/j.sigpro.2021.108453_bib0003 article-title: Parameter estimation in wireless sensor networks with faulty transducers: a distributed EM approach publication-title: Signal Process. doi: 10.1016/j.sigpro.2017.10.012 |
| SSID | ssj0001360 |
| Score | 2.3706625 |
| Snippet | The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108453 |
| SubjectTerms | Expectation maximization algorithm Fourier series Maximum likelihood |
| Title | Expectation maximization algorithm over Fourier series (EMoFS) |
| URI | https://dx.doi.org/10.1016/j.sigpro.2021.108453 |
| Volume | 194 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF5KvehBfGJ9lD140MPabLK72VyEUhqq0l5qobeQ7E5qpC9sBU_-dnfz0Aqi4DXMQJgsM7OZ7_sGoUvhWn6uxwl3wCVMpIrEOnYIuBq4rySN83-6_YHojdj9mI9rqFNxYSysssz9RU7Ps3X5pFVGs7XMstbQEnGoHSPRfHxlNUEZ8-0pv3n_gnlQL2cKW2NirSv6XI7xWmUTk6fMLdGlFmzHuPdzedooOeEe2i17RdwuXmcf1WB-gHY2FAQP0a2VKlbFOB3P4rdsVvIqcTydLMzF_2mGLUgTh8VuOmxPHKzwVbe_CIfXR2gUdh87PVKuRCDK9PZrIrmfBKaF8x0fmKYBCM0AqGZOnGqptSl5impt2gyuhOQgeOAxnoKUSRAkArxjVJ8v5nCCsGmERKCYlQBLmCPTRMQUhKMDIRMOzG8gr4pEpEq9cLu2YhpVwLDnqIhfZOMXFfFrIPLptSz0Mv6w96sgR9--e2RS-q-ep__2PEPbriUx5LDFc1Rfv7zChWkt1kkzPztNtNW-e-gNPgBDL80j |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JasMwEB3S5ND2ULrSdPWhh_YgYtmSLF8KIcQ4zXJJArkZ25JTl2w0KfTzK3kJKZQWejUaMGPx5sl6bwbggVnan2tTRE1pIcKSGIUiNJG0hKROzHGY_dPtD5g_Ji8TOqlAq_TCaFllgf05pmdoXTxpFNlsrNK0MdRGHKyvkXB2fUX2oKa7U9Eq1Jqdrj_YAjK2M7OwXo90QOmgy2Re63SqoEodFC2s9XaE2j9XqJ2q4x3DUUEXjWb-RidQkYtTONxpIngGz7pbcZzfqBvz8DOdF9ZKI5xNl-rs_zo3tE7T8PLxdIbedHJtPLb7S2_4dA5jrz1q-aiYioBiRe83iFMnchWLc0xHEoFdyQSREgtihongQqiqF2MhFNOgMeNUMurahCaS88h1IybtC6gulgt5CYbiQsyNie4CFhGTJxELsWSmcBmPqCROHewyE0FctAzXkytmQakNewvy_AU6f0GevzqgbdQqb5nxx3qnTHLw7dMHCtV_jbz6d-Q97Pujfi_odQbdaziwtKchUzHeQHXz_iFvFdPYRHfFTvoCXfvP1A |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Expectation+maximization+algorithm+over+Fourier+series+%28EMoFS%29&rft.jtitle=Signal+processing&rft.au=Yildiz%2C+M%C3%BCrsel&rft.date=2022-05-01&rft.issn=0165-1684&rft.volume=194&rft.spage=108453&rft_id=info:doi/10.1016%2Fj.sigpro.2021.108453&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_sigpro_2021_108453 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0165-1684&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0165-1684&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0165-1684&client=summon |