Independent component analysis: recent advances
Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of...
Saved in:
| Published in | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 371; no. 1984; p. 20110534 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
England
The Royal Society Publishing
13.02.2013
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model. |
|---|---|
| AbstractList | Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model.Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model. Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model. Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model. |
| Author | Hyvärinen, Aapo |
| AuthorAffiliation | Department of Computer Science, Department of Mathematics and Statistics, and HIIT , University of Helsinki , Helsinki, Finland |
| AuthorAffiliation_xml | – name: Department of Computer Science, Department of Mathematics and Statistics, and HIIT , University of Helsinki , Helsinki, Finland |
| Author_xml | – sequence: 1 givenname: Aapo surname: Hyvärinen fullname: Hyvärinen, Aapo email: aapo.hyvarinen@helsinki.fi organization: Department of Computer Science, Department of Mathematics and Statistics, and HIIT, University of Helsinki, Helsinki, Finland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23277597$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UUtv1DAQtlARfcCVI-qRS7a2x3YcDkhVoaWiEhIUxG3kdRxwm42DnazY_nqcpqwoqEiWPfJ8j9F8-2SnC50j5DmjC0YrfRTTYBacMragEsQjssdEyQpeKb6Ta1CikBS-7pL9lK5ohinJn5BdDrwsZVXukaPzrna9y1c3HNqw6rN8rkxn2k3y6dVhdPb2o16bzrr0lDxuTJvcs7v3gHw-fXt58q64-HB2fnJ8UVgFeiiWEgw1SytYowzVOh9JWVXzpqaVqClnVtXVErTjINXS1BKkMU1TWt0ooSQckNezbj8uV66eZoimxT76lYkbDMbj_U7nv-O3sEaQoAXoLPDyTiCGH6NLA658sq5tTefCmJDxEhiTAliGvvjTa2vye0sZsJgBNoaUomu2EEZxigGnGHCKAacYMkH8RbB-MIMP06y-fZh2PdNi2OTlBuvdsMGrMMYcR8KPny6P11AyzyotkGpgVEjGFd74fpbKTfQpjQ5vIffl_3WD_7k9OGMxs3wa3M_tIky8RlVCKfFLNtYg9Xt-VuEb-AWXV8-v |
| CitedBy_id | crossref_primary_10_1007_s13209_021_00247_3 crossref_primary_10_1016_j_ces_2016_02_038 crossref_primary_10_1002_hbm_26644 crossref_primary_10_1088_2633_1357_abc0b8 crossref_primary_10_37391_ijeer_100336 crossref_primary_10_1016_j_specom_2017_06_007 crossref_primary_10_1016_j_isci_2020_101656 crossref_primary_10_1080_19585969_2023_2168135 crossref_primary_10_1016_j_jsv_2016_01_046 crossref_primary_10_1007_s10548_016_0512_4 crossref_primary_10_1128_msystems_00942_23 crossref_primary_10_1214_23_AOS2256 crossref_primary_10_1103_PhysRevLett_116_104101 crossref_primary_10_1109_ACCESS_2021_3102643 crossref_primary_10_3389_fams_2019_00040 crossref_primary_10_1016_j_ymssp_2024_111376 crossref_primary_10_1016_j_chemolab_2015_01_003 crossref_primary_10_1007_s00168_015_0678_9 crossref_primary_10_3390_s20061545 crossref_primary_10_1007_s40010_017_0433_y crossref_primary_10_1016_j_patcog_2023_109773 crossref_primary_10_1016_j_patcog_2023_109376 crossref_primary_10_1016_j_saa_2023_122584 crossref_primary_10_1016_j_jedc_2022_104434 crossref_primary_10_1016_j_aca_2019_03_009 crossref_primary_10_1007_s12559_022_10066_8 crossref_primary_10_1007_s10055_021_00538_x crossref_primary_10_3233_JAD_161291 crossref_primary_10_1016_j_trac_2013_03_013 crossref_primary_10_1021_acsomega_0c04982 crossref_primary_10_1007_s40799_020_00395_4 crossref_primary_10_1029_2024EA003552 crossref_primary_10_1371_journal_pcbi_1005189 crossref_primary_10_3847_1538_3881_aa738b crossref_primary_10_1016_j_procs_2018_08_009 crossref_primary_10_7554_eLife_97709 crossref_primary_10_1186_s43251_024_00148_y crossref_primary_10_3847_1538_4357_aa692d crossref_primary_10_1002_2017WR021293 crossref_primary_10_1038_s41598_025_86986_w crossref_primary_10_1186_s12859_022_04733_8 crossref_primary_10_1038_s41551_022_00962_7 crossref_primary_10_1137_21M1398860 crossref_primary_10_1080_14789450_2022_2070476 crossref_primary_10_1371_journal_pone_0297996 crossref_primary_10_1016_j_measurement_2023_112504 crossref_primary_10_1142_S2424922X20410041 crossref_primary_10_1002_tee_23915 crossref_primary_10_1016_j_apergo_2024_104418 crossref_primary_10_1515_ijnaoe_2015_0010 crossref_primary_10_1080_01691864_2016_1172732 crossref_primary_10_1162_imag_a_00182 crossref_primary_10_3389_fmolb_2022_907150 crossref_primary_10_1137_20M1347486 crossref_primary_10_1214_24_AOS2373 crossref_primary_10_1016_j_jebo_2024_106786 crossref_primary_10_1177_0954406215610788 crossref_primary_10_3233_JAE_162201 crossref_primary_10_1016_j_jneumeth_2024_110097 crossref_primary_10_1007_s10548_019_00750_8 crossref_primary_10_1190_INT_2018_0109_1 crossref_primary_10_1002_2016JB013765 crossref_primary_10_1016_j_scitotenv_2022_157848 crossref_primary_10_1017_jfm_2014_355 crossref_primary_10_1109_LSP_2025_3540370 crossref_primary_10_1016_j_jneumeth_2016_06_006 crossref_primary_10_1117_1_JRS_13_016516 crossref_primary_10_1016_j_procs_2018_07_270 crossref_primary_10_1146_annurev_fluid_030121_015835 crossref_primary_10_1021_acs_iecr_2c00335 crossref_primary_10_1111_jmi_12167 crossref_primary_10_5005_jp_journals_10028_1674 crossref_primary_10_1007_s12021_019_09416_z crossref_primary_10_1080_19479832_2021_1915395 crossref_primary_10_1175_JCLI_D_15_0417_1 crossref_primary_10_3389_frsip_2022_984901 crossref_primary_10_1016_j_neuroimage_2014_05_068 crossref_primary_10_19113_sdufenbed_699241 crossref_primary_10_26636_jtit_2017_120717 crossref_primary_10_5121_mlaij_2023_10101 crossref_primary_10_1109_JSTARS_2023_3336916 crossref_primary_10_1016_j_ymssp_2024_111265 crossref_primary_10_1016_j_neuroimage_2015_01_013 crossref_primary_10_1109_ACCESS_2021_3115760 crossref_primary_10_1002_mas_21602 crossref_primary_10_1021_acs_jpca_7b06447 crossref_primary_10_1016_j_jimonfin_2016_03_003 crossref_primary_10_1016_j_neucom_2020_12_026 crossref_primary_10_1016_j_sigpro_2014_05_022 crossref_primary_10_1029_2019JB018139 crossref_primary_10_1002_mrm_29912 crossref_primary_10_1002_wics_1440 crossref_primary_10_1364_BOE_422170 crossref_primary_10_1109_ACCESS_2021_3107294 crossref_primary_10_13168_AGG_2024_0007 crossref_primary_10_1109_ACCESS_2019_2949814 crossref_primary_10_3389_fnins_2017_00685 crossref_primary_10_1016_j_egyai_2024_100424 crossref_primary_10_1007_s10548_016_0497_z crossref_primary_10_1007_s13311_023_01433_w crossref_primary_10_1016_j_media_2015_08_002 crossref_primary_10_1049_el_2020_0618 crossref_primary_10_1093_jrsssb_qkad037 crossref_primary_10_1016_j_oregeorev_2020_103530 crossref_primary_10_1016_j_neubiorev_2018_06_013 crossref_primary_10_1155_2014_579652 crossref_primary_10_3389_fnhum_2024_1338966 crossref_primary_10_2333_bhmk_41_65 crossref_primary_10_1002_jnr_24669 crossref_primary_10_1109_TGRS_2024_3471982 crossref_primary_10_3390_s17102224 crossref_primary_10_7554_eLife_97107 crossref_primary_10_1007_s00422_014_0608_4 crossref_primary_10_3414_ME13_02_0037 crossref_primary_10_1016_j_neucom_2017_07_072 crossref_primary_10_1021_acsomega_7b01057 crossref_primary_10_1038_s41562_024_01952_2 crossref_primary_10_1080_10106049_2018_1434684 crossref_primary_10_1038_s41598_021_03613_0 crossref_primary_10_1111_joa_12900 crossref_primary_10_1016_j_jvcir_2016_02_006 crossref_primary_10_7554_eLife_97709_3 crossref_primary_10_1093_jrsssb_qkad062 crossref_primary_10_1007_s00371_023_03127_y crossref_primary_10_3389_fnins_2018_00429 crossref_primary_10_1002_2015WR017302 crossref_primary_10_1007_s11694_013_9143_6 crossref_primary_10_1002_ldr_3871 crossref_primary_10_1016_j_jeconom_2022_02_010 crossref_primary_10_1115_1_4027545 crossref_primary_10_1177_0305735619839141 crossref_primary_10_1109_JSEN_2019_2909555 crossref_primary_10_1007_s00216_017_0275_0 crossref_primary_10_1016_j_jprocont_2017_12_001 crossref_primary_10_1016_j_eswa_2016_07_008 crossref_primary_10_1109_OJSP_2020_3038369 crossref_primary_10_3233_JAD_210148 crossref_primary_10_47836_pjst_31_5_08 crossref_primary_10_1007_s12517_015_2269_6 crossref_primary_10_1214_14_EJS932 crossref_primary_10_1088_1742_6596_1117_1_012009 crossref_primary_10_3390_rs10081186 crossref_primary_10_1016_j_ymssp_2019_106485 crossref_primary_10_3389_fped_2017_00159 crossref_primary_10_1016_j_knosys_2021_108040 crossref_primary_10_1016_j_jeconom_2024_105803 crossref_primary_10_7554_eLife_97107_3 crossref_primary_10_1109_TIM_2013_2293236 crossref_primary_10_1016_j_strueco_2023_04_015 crossref_primary_10_1016_j_jedc_2022_104530 crossref_primary_10_1109_TII_2021_3121509 crossref_primary_10_1109_ACCESS_2023_3253891 crossref_primary_10_1007_s12517_022_09889_4 crossref_primary_10_1093_bioadv_vbae081 crossref_primary_10_1007_s10895_018_2271_y crossref_primary_10_1016_j_frl_2022_103399 crossref_primary_10_1016_j_measurement_2024_114241 crossref_primary_10_1080_13662716_2018_1459295 crossref_primary_10_33111_nfmte_2023_067 crossref_primary_10_1007_s12065_021_00607_9 crossref_primary_10_1016_j_aca_2019_02_023 crossref_primary_10_1038_s41582_022_00753_3 crossref_primary_10_1016_j_jhydrol_2022_128914 crossref_primary_10_1039_C7AN00274B |
| Cites_doi | 10.1109/TNN.2003.810616 10.1142/SO129065700000028 10.1162/089976606775093936 10.1109/31.76486 10.1016/j.neuroimage.2004.03.027 10.1007/978-1-84882-491-1 10.1016/0165-1684(94)90029-9 10.1002/0471221317 10.1162/neco.1995.7.6.1129 10.1117/12.507475 10.1109/TBME.2002.805480 10.1016/j.neuroimage.2008.07.032 10.1016/S0893-6080(00)00026-5 10.1016/j.neuroimage.2004.10.042 10.1016/j.neucom.2011.11.005 10.1023/A:1018647011077 10.1016/j.neuroimage.2004.10.043 10.1016/j.tics.2007.09.004 10.1162/NECO_a_00010 10.1109/78.554307 10.1214/009053606000000939 10.1162/089976606774841620 10.1109/78.942614 10.1038/44565 10.1016/j.neuroimage.2008.10.057 10.1109/JSTSP.2008.2005346 10.1016/0165-1684(91)90079-X 10.1162/089976602760128018 10.1162/089976601300014385 10.1198/000313001300339932 10.1162/089976601750264992 10.1109/LSP.2004.830118 10.1214/aoms/1177706099 10.1145/2001269.2001295 10.1162/0899766053011474 10.1038/nature07481 10.1016/j.neuroimage.2009.08.028 10.1162/089976600300015312 10.1007/978-3-642-28551-6_20 10.1109/TIT.2005.864440 10.1016/j.sigpro.2005.02.003 10.1126/science.1127647 10.1162/089976601300014394 10.1109/72.761722 10.1016/j.neuroimage.2011.05.086 10.1016/S0042-6989(02)00017-2 10.1088/0954-898X/5/4/008 10.1002/env.3170050203 10.1103/PhysRevE.70.066123 10.1109/72.925558 10.1016/S0169-7439(96)00044-5 10.1016/j.neuroimage.2011.06.068 10.1109/TBME.2010.2046325 10.1016/j.sigpro.2003.10.010 10.1109/78.599941 10.1002/9780470747278 10.1007/978-3-642-22092-0_46 10.1109/97.566704 10.1007/978-3-642-00599-2_33 |
| ContentType | Journal Article |
| Copyright | 2012 |
| Copyright_xml | – notice: 2012 |
| DBID | BSCLL AAYXX CITATION NPM 7X8 5PM |
| DOI | 10.1098/rsta.2011.0534 |
| DatabaseName | Istex CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Mathematics Sciences (General) Physics |
| DocumentTitleAlternate | Independent Component Analysis |
| EISSN | 1471-2962 |
| ExternalDocumentID | PMC3538438 23277597 10_1098_rsta_2011_0534 ark_67375_V84_8358K2G9_D |
| Genre | Journal Article |
| GroupedDBID | --- -~X 0R~ 18M 2WC 4.4 5VS AACGO AANCE ABBHK ABFAN ABPLY ABTLG ABXSQ ABYWD ACGFO ACIWK ACMTB ACNCT ACQIA ACTMH ADACV ADBBV ADODI ADULT AELPN AEUPB AEXZC AFFNX AFVYC AJZGM ALMA_UNASSIGNED_HOLDINGS ALMYZ AQVQM BGBPD BSCLL BTFSW DCCCD DIK DOOOF DQDLB DSRWC EBS ECEWR EJD F5P H13 HH5 HQ6 HZ~ IPSME JAAYA JBMMH JENOY JHFFW JKQEH JLS JLXEF JMS JPM JSG JSODD JST K-O KQ8 MRS MV1 NSAHA O9- OK1 OP1 P2P RHF RRY SA0 TN5 TR2 V1E W8F XSW YNT ~02 ABPTK ABXXB ADZLD AFXKK DNJUQ DWIUU EFSUC AAWIL AAYXX ACHIC ACRPL ADNMO ADQXQ AGLNM AGPVY AGQPQ AIHAF ALRMG CITATION NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c638t-b53a0abc41f6a0880885019d2fd094d021c6d9b38e2356bad535aaff7c8f64653 |
| ISSN | 1364-503X |
| IngestDate | Thu Aug 21 18:06:33 EDT 2025 Thu Jul 10 21:33:13 EDT 2025 Thu Apr 03 07:09:56 EDT 2025 Tue Jul 01 01:48:18 EDT 2025 Thu Apr 24 23:02:44 EDT 2025 Wed Jan 17 02:37:37 EST 2024 Tue May 24 16:18:01 EDT 2022 Tue Sep 24 00:40:24 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1984 |
| Language | English |
| License | open-access: © 2012 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited. 2012 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c638t-b53a0abc41f6a0880885019d2fd094d021c6d9b38e2356bad535aaff7c8f64653 |
| Notes | istex:479199FCB8A72590D83ECB0AD2685C5F22D81959 href:rsta20110534.pdf ark:/67375/V84-8358K2G9-D ArticleID:rsta20110534 One contribution of 17 to a Discussion Meeting Issue 'Signal processing and inference for the physical sciences'. Discussion Meeting Issue 'Signal processing and inference for the physical sciences' organized and edited by Nick S. Jones and Thomas J. Maccarone ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physical sciences’. |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC3538438 |
| PMID | 23277597 |
| PQID | 1273115431 |
| PQPubID | 23479 |
| ParticipantIDs | crossref_primary_10_1098_rsta_2011_0534 istex_primary_ark_67375_V84_8358K2G9_D pubmed_primary_23277597 crossref_citationtrail_10_1098_rsta_2011_0534 royalsociety_journals_RSTAv371i1984_0831045126_zip_rsta_371_issue_1984_rsta_2011_0534_rsta_2011_0534 royalsociety_journals_10_1098_rsta_2011_0534 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3538438 proquest_miscellaneous_1273115431 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2013-02-13 20130213 2013-Feb-13 |
| PublicationDateYYYYMMDD | 2013-02-13 |
| PublicationDate_xml | – month: 02 year: 2013 text: 2013-02-13 day: 13 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences |
| PublicationTitleAbbrev | Proc. R. Soc. A |
| PublicationTitleAlternate | Proc. R. Soc. A |
| PublicationYear | 2013 |
| Publisher | The Royal Society Publishing |
| Publisher_xml | – name: The Royal Society Publishing |
| References | Hastie T (e_1_3_1_81_2) 2003 Shimizu S (e_1_3_1_17_2) 2006; 7 Amari S-I (e_1_3_1_13_2) 1996 e_1_3_1_66_2 e_1_3_1_89_2 e_1_3_1_22_2 Harshman RA (e_1_3_1_33_2) 1970; 16 e_1_3_1_45_2 e_1_3_1_68_2 e_1_3_1_87_2 e_1_3_1_8_2 e_1_3_1_85_2 e_1_3_1_41_2 e_1_3_1_64_2 e_1_3_1_4_2 Zhang K (e_1_3_1_24_2) 2009 Ranzato M (e_1_3_1_46_2) 2010 e_1_3_1_6_2 Hoyer PO (e_1_3_1_92_2) 2004; 5 Hyvärinen A (e_1_3_1_42_2) 2006 Hyvärinen A (e_1_3_1_50_2) 2005; 6 e_1_3_1_26_2 e_1_3_1_47_2 e_1_3_1_2_2 e_1_3_1_28_2 e_1_3_1_49_2 Comon P (e_1_3_1_5_2) 2010 Lacerda G (e_1_3_1_18_2) 2008 Hyvärinen A (e_1_3_1_14_2) 2010 Gutmann MU (e_1_3_1_51_2) 2012; 13 e_1_3_1_70_2 e_1_3_1_93_2 Hyvärinen A (e_1_3_1_54_2) 1998 Plumbley MD (e_1_3_1_94_2) 2010 e_1_3_1_55_2 e_1_3_1_78_2 e_1_3_1_34_2 e_1_3_1_57_2 e_1_3_1_76_2 e_1_3_1_11_2 e_1_3_1_30_2 e_1_3_1_72_2 Zoran D (e_1_3_1_62_2) 2010; 22 e_1_3_1_15_2 e_1_3_1_36_2 e_1_3_1_38_2 Pham D-T (e_1_3_1_69_2) 2002 Learned-Miller EG (e_1_3_1_82_2) 2003; 4 e_1_3_1_80_2 e_1_3_1_21_2 e_1_3_1_44_2 e_1_3_1_65_2 Bach FR (e_1_3_1_61_2) 2003; 4 Lahat D (e_1_3_1_59_2) 2012 e_1_3_1_67_2 e_1_3_1_88_2 e_1_3_1_7_2 e_1_3_1_40_2 e_1_3_1_86_2 e_1_3_1_9_2 Yeredor A (e_1_3_1_37_2) 2010 e_1_3_1_63_2 e_1_3_1_29_2 Donoho DL (e_1_3_1_91_2) 2004 e_1_3_1_3_2 Bach FR (e_1_3_1_83_2) 2002; 3 e_1_3_1_25_2 e_1_3_1_48_2 Shimizu S (e_1_3_1_19_2) 2011; 12 e_1_3_1_27_2 Hoyer PO (e_1_3_1_23_2) 2009 Kawanabe M (e_1_3_1_53_2) 2005; 6 Sasaki H (e_1_3_1_60_2) 2012 Kisilev P (e_1_3_1_73_2) 2003; 4 e_1_3_1_71_2 e_1_3_1_90_2 Varoquaux G (e_1_3_1_31_2) 2011 Hyvärinen A (e_1_3_1_32_2) 2012 Hyvärinen A (e_1_3_1_20_2) 2010; 11 e_1_3_1_79_2 e_1_3_1_35_2 e_1_3_1_56_2 e_1_3_1_77_2 e_1_3_1_12_2 e_1_3_1_75_2 e_1_3_1_10_2 e_1_3_1_52_2 Gretton A (e_1_3_1_84_2) 2008 Gruber P (e_1_3_1_43_2) 2009 e_1_3_1_16_2 e_1_3_1_58_2 Gribonval R (e_1_3_1_74_2) 2010 e_1_3_1_39_2 19020501 - Nature. 2009 Jan 1;457(7225):83-6 20569179 - Neural Comput. 2010 Sep 1;22(9):2308-33 21761686 - Inf Process Med Imaging. 2011;22:562-73 10935923 - Neural Comput. 2000 Jul;12(7):1705-20 10798706 - Int J Neural Syst. 2000 Feb;10(1):1-8 18249888 - IEEE Trans Neural Netw. 2001;12(3):559-66 18252563 - IEEE Trans Neural Netw. 1999;10(3):626-34 18707006 - Neuroimage. 2008 Nov 15;43(3):497-508 15697450 - Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Dec;70(6 Pt 2):066123 11255573 - Neural Comput. 2001 Apr;13(4):863-82 16873662 - Science. 2006 Jul 28;313(5786):504-7 15734364 - Neuroimage. 2005 Mar;25(1):294-311 11440596 - Neural Comput. 2001 Jul;13(7):1527-58 10946390 - Neural Netw. 2000 May-Jun;13(4-5):411-30 19699307 - Neuroimage. 2010 Jan 1;49(1):257-71 7584893 - Neural Comput. 1995 Nov;7(6):1129-59 21704714 - Neuroimage. 2011 Sep 1;58(1):122-36 20483681 - IEEE Trans Biomed Eng. 2010 Aug;57(8):1954-63 19059344 - Neuroimage. 2009 Mar;45(1 Suppl):S163-72 15720773 - Neural Comput. 2005 Feb;17(2):397-423 11255574 - Neural Comput. 2001 Apr;13(4):883-98 15219593 - Neuroimage. 2004 Jul;22(3):1214-22 12549733 - IEEE Trans Biomed Eng. 2002 Dec;49(12 Pt 2):1514-25 15734355 - Neuroimage. 2005 Mar;25(1):193-205 18238037 - IEEE Trans Neural Netw. 2003;14(3):534-43 12074953 - Vision Res. 2002 Jun;42(12):1593-605 10548103 - Nature. 1999 Oct 21;401(6755):788-91 12180402 - Neural Comput. 2002 Aug;14(8):1771-800 16378519 - Neural Comput. 2006 Feb;18(2):381-414 17921042 - Trends Cogn Sci. 2007 Oct;11(10):428-34 21745580 - Neuroimage. 2011 Oct 1;58(3):838-48 |
| References_xml | – start-page: 1 volume-title: In Proc. Asian Conf. Machine Learning, Tokyo, Japan year: 2010 ident: e_1_3_1_14_2 – start-page: 135 volume-title: In Proc. Int. Conf. on Artificial Neural Networks (ICANN’98), Skövde, Sweden year: 1998 ident: e_1_3_1_54_2 – ident: e_1_3_1_93_2 doi: 10.1109/TNN.2003.810616 – ident: e_1_3_1_77_2 doi: 10.1142/SO129065700000028 – volume-title: In Advances in neural information processing 16 (Proc. NIPS*2003) year: 2004 ident: e_1_3_1_91_2 – volume: 4 start-page: 1205 year: 2003 ident: e_1_3_1_61_2 article-title: Beyond independent components: trees and clusters publication-title: J. Mach. Learn. Res. – volume-title: Handbook of blind source separation year: 2010 ident: e_1_3_1_74_2 – ident: e_1_3_1_47_2 doi: 10.1162/089976606775093936 – ident: e_1_3_1_66_2 doi: 10.1109/31.76486 – ident: e_1_3_1_25_2 doi: 10.1016/j.neuroimage.2004.03.027 – volume-title: In Proc. Asian Conf. on Machine Learning, Singapore. year: 2012 ident: e_1_3_1_60_2 – ident: e_1_3_1_39_2 doi: 10.1007/978-1-84882-491-1 – ident: e_1_3_1_6_2 doi: 10.1016/0165-1684(94)90029-9 – ident: e_1_3_1_4_2 doi: 10.1002/0471221317 – volume: 12 start-page: 1225 year: 2011 ident: e_1_3_1_19_2 article-title: DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_29_2 – ident: e_1_3_1_9_2 doi: 10.1162/neco.1995.7.6.1129 – ident: e_1_3_1_72_2 doi: 10.1117/12.507475 – ident: e_1_3_1_15_2 – ident: e_1_3_1_26_2 doi: 10.1109/TBME.2002.805480 – ident: e_1_3_1_55_2 doi: 10.1016/j.neuroimage.2008.07.032 – ident: e_1_3_1_3_2 doi: 10.1016/S0893-6080(00)00026-5 – ident: e_1_3_1_28_2 doi: 10.1016/j.neuroimage.2004.10.042 – volume: 5 start-page: 1457 year: 2004 ident: e_1_3_1_92_2 article-title: Non-negative matrix factorization with sparseness constraints publication-title: J. Mach. Learn. Res. – volume: 22 volume-title: Advances in neural information processing systems year: 2010 ident: e_1_3_1_62_2 – volume-title: In Proc. 24th Conf. Uncertainty in Artificial Intelligence (UAI2008), Helsinki, Finland. year: 2008 ident: e_1_3_1_18_2 – ident: e_1_3_1_22_2 doi: 10.1016/j.neucom.2011.11.005 – volume-title: In Proc. 13th Int. Conf. on Artificial Intelligence and Statistics (AISTATS2010), Sardinia, Italy, 13–15 May 2010. year: 2010 ident: e_1_3_1_46_2 – ident: e_1_3_1_56_2 – ident: e_1_3_1_78_2 doi: 10.1023/A:1018647011077 – ident: e_1_3_1_34_2 doi: 10.1016/j.neuroimage.2004.10.043 – volume: 4 start-page: 1271 year: 2003 ident: e_1_3_1_82_2 article-title: ICA using spacings estimates of entropy publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_63_2 doi: 10.1016/j.tics.2007.09.004 – volume-title: Handbook of blind source separation year: 2010 ident: e_1_3_1_37_2 – ident: e_1_3_1_48_2 doi: 10.1162/NECO_a_00010 – ident: e_1_3_1_35_2 doi: 10.1109/78.554307 – ident: e_1_3_1_79_2 doi: 10.1214/009053606000000939 – ident: e_1_3_1_58_2 doi: 10.1162/089976606774841620 – ident: e_1_3_1_36_2 doi: 10.1109/78.942614 – ident: e_1_3_1_88_2 doi: 10.1038/44565 – ident: e_1_3_1_30_2 doi: 10.1016/j.neuroimage.2008.10.057 – ident: e_1_3_1_38_2 doi: 10.1109/JSTSP.2008.2005346 – volume: 11 start-page: 1709 year: 2010 ident: e_1_3_1_20_2 article-title: Estimation of a structural vector autoregression model using non-Gaussianity publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_2_2 doi: 10.1016/0165-1684(91)90079-X – volume: 16 start-page: 1 year: 1970 ident: e_1_3_1_33_2 article-title: Foundations of the PARAFAC procedure: models and conditions for an explanatory multimodal factor analysis publication-title: UCLA Working Papers Phonetics – volume: 7 start-page: 2003 year: 2006 ident: e_1_3_1_17_2 article-title: A linear non-Gaussian acyclic model for causal discovery publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_49_2 doi: 10.1162/089976602760128018 – ident: e_1_3_1_71_2 doi: 10.1162/089976601300014385 – ident: e_1_3_1_16_2 doi: 10.1198/000313001300339932 – ident: e_1_3_1_41_2 doi: 10.1162/089976601750264992 – volume: 6 start-page: 695 year: 2005 ident: e_1_3_1_50_2 article-title: Estimation of non-normalized statistical models using score matching publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_7_2 doi: 10.1109/LSP.2004.830118 – volume-title: Advances in neural information processing systems year: 1996 ident: e_1_3_1_13_2 – ident: e_1_3_1_70_2 doi: 10.1214/aoms/1177706099 – volume: 13 start-page: 307 year: 2012 ident: e_1_3_1_51_2 article-title: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_65_2 doi: 10.1145/2001269.2001295 – ident: e_1_3_1_44_2 doi: 10.1162/0899766053011474 – ident: e_1_3_1_45_2 doi: 10.1038/nature07481 – volume-title: Advances in neural information processing systems year: 2008 ident: e_1_3_1_84_2 – ident: e_1_3_1_75_2 doi: 10.1016/j.neuroimage.2009.08.028 – volume-title: In Proc. Eur. Symp. Artificial Neural Networks, Bruges, Belgium. year: 2006 ident: e_1_3_1_42_2 – start-page: 151 volume-title: In Proc. Int. Conf. on Digital Signal Processing (DSP2002) year: 2002 ident: e_1_3_1_69_2 – volume: 3 start-page: 1 year: 2002 ident: e_1_3_1_83_2 article-title: Kernel independent component analysis publication-title: J. Mach. Learn. Res. – volume-title: Handbook of blind source separation year: 2010 ident: e_1_3_1_94_2 – volume-title: Handbook of blind source separation. year: 2010 ident: e_1_3_1_5_2 – ident: e_1_3_1_40_2 doi: 10.1162/089976600300015312 – start-page: 155 volume-title: Latent variable analysis and signal separation year: 2012 ident: e_1_3_1_59_2 doi: 10.1007/978-3-642-28551-6_20 – ident: e_1_3_1_76_2 doi: 10.1109/TIT.2005.864440 – ident: e_1_3_1_68_2 doi: 10.1016/j.sigpro.2005.02.003 – ident: e_1_3_1_64_2 doi: 10.1126/science.1127647 – ident: e_1_3_1_67_2 doi: 10.1162/089976601300014394 – volume-title: Advances in neural information processing systems year: 2009 ident: e_1_3_1_23_2 – volume-title: In Human Brain Mapping Meeting, Beijing, China, 10–14 June 2012. year: 2012 ident: e_1_3_1_32_2 – ident: e_1_3_1_12_2 doi: 10.1109/72.761722 – ident: e_1_3_1_27_2 doi: 10.1016/j.neuroimage.2011.05.086 – ident: e_1_3_1_89_2 doi: 10.1016/S0042-6989(02)00017-2 – start-page: 647 volume-title: In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), Montréal, Canada year: 2009 ident: e_1_3_1_24_2 – ident: e_1_3_1_10_2 doi: 10.1088/0954-898X/5/4/008 – ident: e_1_3_1_86_2 doi: 10.1002/env.3170050203 – ident: e_1_3_1_85_2 doi: 10.1103/PhysRevE.70.066123 – volume: 4 start-page: 1339 year: 2003 ident: e_1_3_1_73_2 article-title: A multiscale framework for blind separation of linearly mixed signals publication-title: J. Mach. Learn. Res. – ident: e_1_3_1_80_2 doi: 10.1109/72.925558 – ident: e_1_3_1_87_2 doi: 10.1016/S0169-7439(96)00044-5 – ident: e_1_3_1_21_2 doi: 10.1016/j.neuroimage.2011.06.068 – ident: e_1_3_1_57_2 doi: 10.1109/TBME.2010.2046325 – volume-title: In Advances in neural information processing 15 (Proc. NIPS*2002). year: 2003 ident: e_1_3_1_81_2 – ident: e_1_3_1_52_2 doi: 10.1016/j.sigpro.2003.10.010 – ident: e_1_3_1_8_2 doi: 10.1109/78.599941 – ident: e_1_3_1_90_2 doi: 10.1002/9780470747278 – start-page: 562 volume-title: Information processing in medical imaging year: 2011 ident: e_1_3_1_31_2 doi: 10.1007/978-3-642-22092-0_46 – ident: e_1_3_1_11_2 doi: 10.1109/97.566704 – start-page: 259 volume-title: Proc. Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA2009), Paraty, Brazil year: 2009 ident: e_1_3_1_43_2 doi: 10.1007/978-3-642-00599-2_33 – volume: 6 start-page: 453 year: 2005 ident: e_1_3_1_53_2 article-title: Estimating functions for blind separation when sources have variance dependencies publication-title: J. Mach. Learn. Res. – reference: 10935923 - Neural Comput. 2000 Jul;12(7):1705-20 – reference: 17921042 - Trends Cogn Sci. 2007 Oct;11(10):428-34 – reference: 10798706 - Int J Neural Syst. 2000 Feb;10(1):1-8 – reference: 19059344 - Neuroimage. 2009 Mar;45(1 Suppl):S163-72 – reference: 18238037 - IEEE Trans Neural Netw. 2003;14(3):534-43 – reference: 20569179 - Neural Comput. 2010 Sep 1;22(9):2308-33 – reference: 12180402 - Neural Comput. 2002 Aug;14(8):1771-800 – reference: 18707006 - Neuroimage. 2008 Nov 15;43(3):497-508 – reference: 15219593 - Neuroimage. 2004 Jul;22(3):1214-22 – reference: 16378519 - Neural Comput. 2006 Feb;18(2):381-414 – reference: 11440596 - Neural Comput. 2001 Jul;13(7):1527-58 – reference: 12549733 - IEEE Trans Biomed Eng. 2002 Dec;49(12 Pt 2):1514-25 – reference: 12074953 - Vision Res. 2002 Jun;42(12):1593-605 – reference: 11255574 - Neural Comput. 2001 Apr;13(4):883-98 – reference: 7584893 - Neural Comput. 1995 Nov;7(6):1129-59 – reference: 16873662 - Science. 2006 Jul 28;313(5786):504-7 – reference: 15734355 - Neuroimage. 2005 Mar;25(1):193-205 – reference: 15720773 - Neural Comput. 2005 Feb;17(2):397-423 – reference: 15697450 - Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Dec;70(6 Pt 2):066123 – reference: 19699307 - Neuroimage. 2010 Jan 1;49(1):257-71 – reference: 10946390 - Neural Netw. 2000 May-Jun;13(4-5):411-30 – reference: 18252563 - IEEE Trans Neural Netw. 1999;10(3):626-34 – reference: 10548103 - Nature. 1999 Oct 21;401(6755):788-91 – reference: 19020501 - Nature. 2009 Jan 1;457(7225):83-6 – reference: 11255573 - Neural Comput. 2001 Apr;13(4):863-82 – reference: 15734364 - Neuroimage. 2005 Mar;25(1):294-311 – reference: 21704714 - Neuroimage. 2011 Sep 1;58(1):122-36 – reference: 20483681 - IEEE Trans Biomed Eng. 2010 Aug;57(8):1954-63 – reference: 21761686 - Inf Process Med Imaging. 2011;22:562-73 – reference: 21745580 - Neuroimage. 2011 Oct 1;58(3):838-48 – reference: 18249888 - IEEE Trans Neural Netw. 2001;12(3):559-66 |
| SSID | ssj0011652 |
| Score | 2.5146003 |
| SecondaryResourceType | review_article |
| Snippet | Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally... |
| SourceID | pubmedcentral proquest pubmed crossref royalsociety istex |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 20110534 |
| SubjectTerms | Blind Source Separation Causal Analysis Independent Component Analysis Non-Gaussianity Review |
| Title | Independent component analysis: recent advances |
| URI | https://api.istex.fr/ark:/67375/V84-8358K2G9-D/fulltext.pdf https://royalsocietypublishing.org/doi/full/10.1098/rsta.2011.0534 https://www.ncbi.nlm.nih.gov/pubmed/23277597 https://www.proquest.com/docview/1273115431 https://pubmed.ncbi.nlm.nih.gov/PMC3538438 |
| Volume | 371 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3rb9MwELdgExJ8QGy8wktBQjw0siWxnQffJl6FaYhHN-2bZechKqCtmmyC_fXc2Y6bllYCpKpyEstp-jtf7uy73xHyKEpVmcY0D6RKZMBkXAR5qFiQs6pMqzCUVYb5zocfksERe3_CT5ayS1q1W5yvzCv5H1ThHOCKWbL_gKwbFE5AG_CFb0AYvv8K43euhm2rY8MnYxMxbnhG0NkHfaZPmZ3-pm-KfuyKGGiY2nnd8KaLGzBLC11cJ24m6Nofu6hgwMHe0Wvzh473VZcOsEslpo2L8tWc8HDHvm6dGT_4daY36hmmIBouAzmd9BcisChEHJg8Uqs7acICHuryvk65UlNgpZOiPGN9bYnGBzeLmX-o8jDH9ARM77FEq0sd4RGnPzSwYBSmKTdhvkvk2d2li2QTGtqCPvg032aKEl2Syf1yx-qZ7S3eGDmj7VALBswmzsWfq7yTFUG2M0StMaD1jJjhNXLVeh_-vhGlLXKhGm-TKz1OSjiaA9psk0s6QhhbW_Yt0PhPLVX5s-tkryeBvpNAv5PAF76RP7-Tvxvk6M3r4ctBYEtwBAUo5jZQnMpQqoJFdSLhhQQfDk5BGddlmLMSDMQiKXNFsyqmPFGy5JRLWddpkdUJUvfdJBtjuPVtjKGL6wzZ_gsZMqpqFVGlIikrcFhZXHKPBN3_KgrLT49lUr4LEyeRCYREICQCIfHIE9d_aphZ1vZ8rGFy3eTsG8YzplwcZ0yAD5IdxG9z8cojDzscBWhZ3DqT42py2ogIrHwkrqKRR24ZXN1onWB4JF1A3HVABvfFK-PRV83kTsHcYDTzyPO-bAirYpq1z1Ou7v75y3D_DGbcCOeZ0DUEGRj0iTgfTc0YcFHoqSh0l8Vxlw7vrH3Su-TyXAHcIxvt7LS6D7Z5qx7o-fUbgPbgpg |
| linkProvider | Colorado Alliance of Research Libraries |
| 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=Independent+component+analysis%3A+recent+advances&rft.jtitle=Philosophical+transactions+of+the+Royal+Society+of+London.+Series+A%3A+Mathematical%2C+physical%2C+and+engineering+sciences&rft.au=Hyv%C3%A4rinen%2C+Aapo&rft.date=2013-02-13&rft.issn=1364-503X&rft.volume=371&rft.issue=1984&rft.spage=20110534&rft_id=info:doi/10.1098%2Frsta.2011.0534&rft_id=info%3Apmid%2F23277597&rft.externalDocID=23277597 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1364-503X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1364-503X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1364-503X&client=summon |