Online dictionary learning for Local Coordinate Coding with Locality Coding Adaptors
Dictionary in Local Coordinate Coding (LCC) is important to approximate a non-linear function with linear ones. Optimizing dictionary from predefined coding schemes is a challenge task. This paper focuses on learning dictionary from two Locality Coding Adaptors (LCAs), i.e., locality Gaussian Adapto...
Saved in:
| Published in | Neurocomputing (Amsterdam) Vol. 157; pp. 61 - 69 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.2015
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2015.01.035 |
Cover
| Abstract | Dictionary in Local Coordinate Coding (LCC) is important to approximate a non-linear function with linear ones. Optimizing dictionary from predefined coding schemes is a challenge task. This paper focuses on learning dictionary from two Locality Coding Adaptors (LCAs), i.e., locality Gaussian Adaptor (GA) and locality Euclidean Adaptor (EA), for large-scale and high-dimension datasets. Online dictionary learning is formulated as two cycling steps, local coding and dictionary updating. Both stages scale up gracefully to large-scale datasets with millions of data. The experiments on different applications demonstrate that our method leads to a faster dictionary learning than the classical ones or the state-of-the-art methods. |
|---|---|
| AbstractList | Dictionary in Local Coordinate Coding (LCC) is important to approximate a non-linear function with linear ones. Optimizing dictionary from predefined coding schemes is a challenge task. This paper focuses on learning dictionary from two Locality Coding Adaptors (LCAs), i.e., locality Gaussian Adaptor (GA) and locality Euclidean Adaptor (EA), for large-scale and high-dimension datasets. Online dictionary learning is formulated as two cycling steps, local coding and dictionary updating. Both stages scale up gracefully to large-scale datasets with millions of data. The experiments on different applications demonstrate that our method leads to a faster dictionary learning than the classical ones or the state-of-the-art methods. |
| Author | Zhang, Weigang Yin, Baocai Zhang, Chunjie Pang, Junbiao Qin, Lei Huang, Qingming Qing, Laiyun |
| Author_xml | – sequence: 1 givenname: Junbiao orcidid: 0000-0001-8153-7229 surname: Pang fullname: Pang, Junbiao email: junbiao_pang@bjut.edu.cn organization: Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, No. 100 Pingleyuan Road, Chaoyang District 100124, China – sequence: 2 givenname: Chunjie surname: Zhang fullname: Zhang, Chunjie email: cjzhang@jdl.ac.cn organization: School of Computer and Control Engineering, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China – sequence: 3 givenname: Lei surname: Qin fullname: Qin, Lei email: lqin@jdl.ac.cn organization: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China – sequence: 4 givenname: Weigang surname: Zhang fullname: Zhang, Weigang email: wgzhang@jdl.ac.cn organization: School of Computer Science and Technology, Harbin Institute of Technology at Weihai, No. 2 West Wenhua Road, Weihai 26209, China – sequence: 5 givenname: Laiyun surname: Qing fullname: Qing, Laiyun email: lyqing@ucas.ac.cn organization: School of Computer and Control Engineering, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China – sequence: 6 givenname: Qingming surname: Huang fullname: Huang, Qingming email: qmhuang@jdl.ac.cn organization: Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, No. 100 Pingleyuan Road, Chaoyang District 100124, China – sequence: 7 givenname: Baocai surname: Yin fullname: Yin, Baocai email: ybc@bjut.edu.cn organization: Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, No. 100 Pingleyuan Road, Chaoyang District 100124, China |
| BookMark | eNqFkM1OwzAQhC1UJNrCG3DICyTs2q3jcECqKv6kSL2Us-XaDrhK7coxoL49iQIXDnDa1Y5mtPPNyMQHbwm5RigQkN_sC2_fdTgUFHBZABbAlmdkiqKkuaCCT8gUKrrMKUN6QWZdtwfAEmk1JduNb523mXE6ueBVPGWtVdE7_5o1IWZ10KrN1iFE47xKtl_NoH269DaKLp1-jiujjinE7pKcN6rt7NX3nJOXh_vt-imvN4_P61WdawY85YxXun9J4M6a0mBTUYqsbJhFWCgwANwKYZQWi7IxeqdFaQTXjIoGVLXjnM3J7ZirY-i6aBupXVJDjxSVayWCHPjIvRz5yIGPBJQ9n968-GU-RnfoAfxnuxttti_24WyUnXbWa2tctDpJE9zfAV8HX4TW |
| CitedBy_id | crossref_primary_10_3233_IDA_150296 crossref_primary_10_1109_TCYB_2015_2433926 crossref_primary_10_1007_s12559_020_09780_y |
| Cites_doi | 10.1109/TPAMI.2008.79 10.1016/j.patcog.2012.11.014 10.5244/C.24.36 10.1109/TPAMI.2012.63 10.1109/CVPR.2010.5540018 10.1109/ACV.1994.341300 10.7551/mitpress/7503.003.0105 10.1109/TPAMI.2008.138 10.1016/j.patcog.2012.10.006 10.1109/34.291440 10.1007/s10107-007-0170-0 10.1111/j.2517-6161.1996.tb02080.x 10.1145/1553374.1553463 10.1109/CVPR.2006.68 10.1016/j.neucom.2011.11.031 |
| ContentType | Journal Article |
| Copyright | 2015 Elsevier B.V. |
| Copyright_xml | – notice: 2015 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2015.01.035 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 69 |
| ExternalDocumentID | 10_1016_j_neucom_2015_01_035 S0925231215000569 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN BNPGV CITATION FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW SSH WUQ XPP |
| ID | FETCH-LOGICAL-c306t-369c23181bed7d1f922137f3e104a0d006e88dac847fdcbc87d86c328f0a9b663 |
| IEDL.DBID | AIKHN |
| ISSN | 0925-2312 |
| IngestDate | Thu Apr 24 23:00:17 EDT 2025 Tue Jul 01 04:52:24 EDT 2025 Fri Feb 23 02:28:28 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Locality Coding Adaptor Surrogate function Large scale problem Online training Local Coordinate Coding |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-369c23181bed7d1f922137f3e104a0d006e88dac847fdcbc87d86c328f0a9b663 |
| ORCID | 0000-0001-8153-7229 |
| PageCount | 9 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_neucom_2015_01_035 crossref_primary_10_1016_j_neucom_2015_01_035 elsevier_sciencedirect_doi_10_1016_j_neucom_2015_01_035 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2015-06-01 2015-06-00 |
| PublicationDateYYYYMMDD | 2015-06-01 |
| PublicationDate_xml | – month: 06 year: 2015 text: 2015-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2015 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | D. Donoho, For Most Large Underdetermined Systems of Linear Equations, The Minimal L1-Norm Near-Solution Approximates the Sparsest Near-Solution, Technical Report, Stanford University. Mairal, Bach, Ponce, Sapiro (bib20) 2011; 11 Wei, Chao, Yeh, Wang (bib8) 2013; 46 Zhang, Yao, Sun, Lu (bib15) 2013; 46 Wright, Yang, Ganesh, Sastry, Ma (bib25) 2009; 31 J. Mairal, F. Bach, J. Ponce, G. Sapiro, Online dictionary learning for sparse coding, in: International Conference on Machine Learning, 2009, pp. 87–95. M. Everingham, L. Gool, C. Williams, J. Winn, A. Zisserman, The Pascal Visual Object Classes Challenge 2007 (VOC 2007) Results, 2007. Hull (bib23) 1994; 16 D. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, 1999. J. Wang, J. Yang, K. Yu, F. Lv, T.S. Huang, Y. Gong, Locality-constrained linear coding for image classification, in: IEEE Computer Vision and Pattern Recognition, vol. 2, 2010, pp. 3360–3367. Meyer (bib19) 2000 Tseng, Yun (bib12) 2009; 117 S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2169–2178. Zhang, Yao, Zhou, Sun, Liu (bib6) 2013; 100 D. Donoho, For Most Large Underdetermined Systems of Linear Equations, The Minimal L1-Norm Solution is also the Sparsest Solution, Technical Report, Stanford University. Nesterov (bib18) 2004 L. Ladický, P. Torr, Locally linear support vector machines, in: International Conference on Machine Learning, 2011, pp. 985–992. B. Xie, M. Song, D. Tao, Large-scale dictionary learning for local coordinate coding, in: British Machine Vision Conference, vol. 2, 2010, pp. 1–9. Tibshirani (bib5) 1996; 58 J. Yang, K. Yu, Y. Gong, T.S. Huang, Linear spatial pyramid matching using sparse coding for image classification, in: IEEE Computer Vision and Pattern Recognition, 2009, pp. 1794–1801. J. Pang, Q. Huang, B. Yin, L. Qin, D. Wang, Theoretical analysis of learning local anchors for classification, in: International Conference on Pattern Recognition, 2012, pp. 1803–1806. Gao, Tsang, Chia (bib28) 2013; 35 Lazebnik, Raginsky (bib29) 2009; 31 H. Lee, A. Battle, R. Raina, A. Ng, Efficient sparse coding algorithms, in: Neural Information Processing Systems, 2007, pp. 801–808. F. Samaria, A. Harter, Parameterisation of a stochastic model for human face identification, in: Proceedings of the IEEE Workshop on Applications of Computer Vision, 1994, pp. 138–142. Bottou (bib24) 1998 K. Yu, T. Zhang, Y. Gong, Nonlinear learning using local coordinate coding, in: Neural Information Processing System, 2009, pp. 2223–2231. L. Bottou, O. Bousquet, The trade-offs of large scale learning, in: Neural Information Processing Systems, vol. 20, 2008, pp. 161–168. Nemirovski, Yudin (bib17) 1983 Lazebnik (10.1016/j.neucom.2015.01.035_bib29) 2009; 31 Tibshirani (10.1016/j.neucom.2015.01.035_bib5) 1996; 58 Tseng (10.1016/j.neucom.2015.01.035_bib12) 2009; 117 Gao (10.1016/j.neucom.2015.01.035_bib28) 2013; 35 Meyer (10.1016/j.neucom.2015.01.035_bib19) 2000 10.1016/j.neucom.2015.01.035_bib22 10.1016/j.neucom.2015.01.035_bib21 Wright (10.1016/j.neucom.2015.01.035_bib25) 2009; 31 10.1016/j.neucom.2015.01.035_bib26 10.1016/j.neucom.2015.01.035_bib27 Wei (10.1016/j.neucom.2015.01.035_bib8) 2013; 46 Bottou (10.1016/j.neucom.2015.01.035_bib24) 1998 Zhang (10.1016/j.neucom.2015.01.035_bib6) 2013; 100 10.1016/j.neucom.2015.01.035_bib7 Zhang (10.1016/j.neucom.2015.01.035_bib15) 2013; 46 Hull (10.1016/j.neucom.2015.01.035_bib23) 1994; 16 10.1016/j.neucom.2015.01.035_bib9 10.1016/j.neucom.2015.01.035_bib3 10.1016/j.neucom.2015.01.035_bib2 Mairal (10.1016/j.neucom.2015.01.035_bib20) 2011; 11 Nesterov (10.1016/j.neucom.2015.01.035_bib18) 2004 10.1016/j.neucom.2015.01.035_bib4 10.1016/j.neucom.2015.01.035_bib11 Nemirovski (10.1016/j.neucom.2015.01.035_bib17) 1983 10.1016/j.neucom.2015.01.035_bib1 10.1016/j.neucom.2015.01.035_bib10 10.1016/j.neucom.2015.01.035_bib16 10.1016/j.neucom.2015.01.035_bib13 10.1016/j.neucom.2015.01.035_bib14 |
| References_xml | – reference: D. Donoho, For Most Large Underdetermined Systems of Linear Equations, The Minimal L1-Norm Solution is also the Sparsest Solution, Technical Report, Stanford University. – reference: J. Wang, J. Yang, K. Yu, F. Lv, T.S. Huang, Y. Gong, Locality-constrained linear coding for image classification, in: IEEE Computer Vision and Pattern Recognition, vol. 2, 2010, pp. 3360–3367. – reference: B. Xie, M. Song, D. Tao, Large-scale dictionary learning for local coordinate coding, in: British Machine Vision Conference, vol. 2, 2010, pp. 1–9. – year: 2000 ident: bib19 article-title: Matrix Analysis and Applied Linear Algebra – volume: 11 start-page: 19 year: 2011 end-page: 60 ident: bib20 article-title: Online learning for matrix factorization and sparse coding publication-title: J. Mach. Learn. Res. – volume: 16 start-page: 550 year: 1994 end-page: 554 ident: bib23 article-title: A database for handwritten text recognition research publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 35 start-page: 92 year: 2013 end-page: 104 ident: bib28 article-title: Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: J. Pang, Q. Huang, B. Yin, L. Qin, D. Wang, Theoretical analysis of learning local anchors for classification, in: International Conference on Pattern Recognition, 2012, pp. 1803–1806. – reference: H. Lee, A. Battle, R. Raina, A. Ng, Efficient sparse coding algorithms, in: Neural Information Processing Systems, 2007, pp. 801–808. – reference: K. Yu, T. Zhang, Y. Gong, Nonlinear learning using local coordinate coding, in: Neural Information Processing System, 2009, pp. 2223–2231. – reference: D. Donoho, For Most Large Underdetermined Systems of Linear Equations, The Minimal L1-Norm Near-Solution Approximates the Sparsest Near-Solution, Technical Report, Stanford University. – reference: L. Ladický, P. Torr, Locally linear support vector machines, in: International Conference on Machine Learning, 2011, pp. 985–992. – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: bib5 article-title: Regression shrinkage and selection via the LASSO publication-title: J. R. Stat. Soc. (Ser. B) – reference: L. Bottou, O. Bousquet, The trade-offs of large scale learning, in: Neural Information Processing Systems, vol. 20, 2008, pp. 161–168. – year: 1998 ident: bib24 article-title: Online Algorithms and Stochastic Approximations – reference: F. Samaria, A. Harter, Parameterisation of a stochastic model for human face identification, in: Proceedings of the IEEE Workshop on Applications of Computer Vision, 1994, pp. 138–142. – volume: 31 start-page: 210 year: 2009 end-page: 227 ident: bib25 article-title: Robust face recognition via sparse representation publication-title: IEEE Trans. Pattern Anal. Mach. Learn. – reference: J. Mairal, F. Bach, J. Ponce, G. Sapiro, Online dictionary learning for sparse coding, in: International Conference on Machine Learning, 2009, pp. 87–95. – reference: M. Everingham, L. Gool, C. Williams, J. Winn, A. Zisserman, The Pascal Visual Object Classes Challenge 2007 (VOC 2007) Results, 2007. – reference: S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2169–2178. – volume: 46 start-page: 1772 year: 2013 end-page: 1788 ident: bib15 article-title: Sparse coding based visual tracking publication-title: Pattern Recognit. – reference: D. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, 1999. – volume: 117 start-page: 387 year: 2009 end-page: 423 ident: bib12 article-title: A coordinate gradient descent method for nonsmooth separable minimization publication-title: Math. Prog. – reference: J. Yang, K. Yu, Y. Gong, T.S. Huang, Linear spatial pyramid matching using sparse coding for image classification, in: IEEE Computer Vision and Pattern Recognition, 2009, pp. 1794–1801. – volume: 31 start-page: 1294 year: 2009 end-page: 1309 ident: bib29 article-title: Supervised learning of quantizer codebooks by information loss minimization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 1983 ident: bib17 article-title: Problem Complexity and Method Efficiency in Optimization – year: 2004 ident: bib18 article-title: Introductory Lectures on Convex Optimization – volume: 100 start-page: 31 year: 2013 end-page: 40 ident: bib6 article-title: Robust visual tracking based on online learning sparse representation publication-title: Neurocomputing – volume: 46 start-page: 1277 year: 2013 end-page: 1287 ident: bib8 article-title: Locality-sensitive dictionary learning for sparse representation based classification publication-title: Pattern Recognit. – volume: 31 start-page: 210 issue: 2 year: 2009 ident: 10.1016/j.neucom.2015.01.035_bib25 article-title: Robust face recognition via sparse representation publication-title: IEEE Trans. Pattern Anal. Mach. Learn. doi: 10.1109/TPAMI.2008.79 – volume: 46 start-page: 1277 year: 2013 ident: 10.1016/j.neucom.2015.01.035_bib8 article-title: Locality-sensitive dictionary learning for sparse representation based classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2012.11.014 – ident: 10.1016/j.neucom.2015.01.035_bib16 doi: 10.5244/C.24.36 – ident: 10.1016/j.neucom.2015.01.035_bib21 – volume: 35 start-page: 92 issue: 1 year: 2013 ident: 10.1016/j.neucom.2015.01.035_bib28 article-title: Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.63 – ident: 10.1016/j.neucom.2015.01.035_bib27 doi: 10.1109/CVPR.2010.5540018 – ident: 10.1016/j.neucom.2015.01.035_bib22 doi: 10.1109/ACV.1994.341300 – ident: 10.1016/j.neucom.2015.01.035_bib7 doi: 10.7551/mitpress/7503.003.0105 – ident: 10.1016/j.neucom.2015.01.035_bib14 – volume: 31 start-page: 1294 issue: 7 year: 2009 ident: 10.1016/j.neucom.2015.01.035_bib29 article-title: Supervised learning of quantizer codebooks by information loss minimization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.138 – ident: 10.1016/j.neucom.2015.01.035_bib10 – ident: 10.1016/j.neucom.2015.01.035_bib2 – ident: 10.1016/j.neucom.2015.01.035_bib4 doi: 10.1109/CVPR.2010.5540018 – volume: 46 start-page: 1772 year: 2013 ident: 10.1016/j.neucom.2015.01.035_bib15 article-title: Sparse coding based visual tracking publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2012.10.006 – year: 2004 ident: 10.1016/j.neucom.2015.01.035_bib18 – volume: 16 start-page: 550 issue: 5 year: 1994 ident: 10.1016/j.neucom.2015.01.035_bib23 article-title: A database for handwritten text recognition research publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.291440 – volume: 117 start-page: 387 year: 2009 ident: 10.1016/j.neucom.2015.01.035_bib12 article-title: A coordinate gradient descent method for nonsmooth separable minimization publication-title: Math. Prog. doi: 10.1007/s10107-007-0170-0 – year: 1998 ident: 10.1016/j.neucom.2015.01.035_bib24 – ident: 10.1016/j.neucom.2015.01.035_bib13 – volume: 58 start-page: 267 year: 1996 ident: 10.1016/j.neucom.2015.01.035_bib5 article-title: Regression shrinkage and selection via the LASSO publication-title: J. R. Stat. Soc. (Ser. B) doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: 10.1016/j.neucom.2015.01.035_bib11 doi: 10.1145/1553374.1553463 – ident: 10.1016/j.neucom.2015.01.035_bib26 doi: 10.1109/CVPR.2006.68 – volume: 11 start-page: 19 issue: 1 year: 2011 ident: 10.1016/j.neucom.2015.01.035_bib20 article-title: Online learning for matrix factorization and sparse coding publication-title: J. Mach. Learn. Res. – year: 2000 ident: 10.1016/j.neucom.2015.01.035_bib19 – ident: 10.1016/j.neucom.2015.01.035_bib1 – ident: 10.1016/j.neucom.2015.01.035_bib3 – ident: 10.1016/j.neucom.2015.01.035_bib9 – year: 1983 ident: 10.1016/j.neucom.2015.01.035_bib17 – volume: 100 start-page: 31 year: 2013 ident: 10.1016/j.neucom.2015.01.035_bib6 article-title: Robust visual tracking based on online learning sparse representation publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.11.031 |
| SSID | ssj0017129 |
| Score | 2.1178567 |
| Snippet | Dictionary in Local Coordinate Coding (LCC) is important to approximate a non-linear function with linear ones. Optimizing dictionary from predefined coding... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 61 |
| SubjectTerms | Large scale problem Local Coordinate Coding Locality Coding Adaptor Online training Surrogate function |
| Title | Online dictionary learning for Local Coordinate Coding with Locality Coding Adaptors |
| URI | https://dx.doi.org/10.1016/j.neucom.2015.01.035 |
| Volume | 157 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED71sbDwRpRH5YE1NLETJx6riqq8utBK3awkdlAQSquqHVj47ZxjB4GEQGJLnFyUnOy775y77wCuuBaRUoX2hFluocp9L0lp4YVC01RzGvCau_Nxyifz8G4RLVowamphTFqls_3WptfW2o0MnDYHq7IcPPmCYhRl2BEM8OCiDV3KBI860B3e3k-mnz8T4oBayj0aeUagqaCr07wqvTVpI-gHLX9n3fftBw_1xeuM92HXwUUytG90AC1dHcJe04qBuJV5BDNLGUpUWdcppOs34vpBPBOEpeTBuCwyWmKoWVYIL_HQOC1itmHtRUTjzeBQpSvThOcY5uOb2WjiuYYJXo7If-MxLnL8QESiWsUqKASlAYsLpjHmSn2FC0wniUpz9EiFyrM8iVXCc0aTwk9FhtjjBDrVstKnQFRoqOAUAgrGQp0UQuETdBDGmmWFn6gesEZJMnds4qapxats0sZepFWtNKqVfiBRtT3wPqVWlk3jj_vjRv_y26yQaPB_lTz7t-Q57Jgzmw52AZ3NeqsvEXhssj60r9-DvpteH_YD2DA |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELZKGWDhjShPD6ymiZ2HPVYVVYG2C63UzUpsBxWhtKragYXfzjl2KpAQSGyRH1Fysu8-W999h9BtYkSsdWGIsNst0iogPKMFiYShmUlomFTancNR0p9Ej9N42kDdOhfG0iq973c-vfLWvqXtrdlezGbt50BQOEVZdQQLPBKxhbajmKWW13f3seF5hGlIneAejYkdXufPVSSv0qwtaQSioFPvrKq-_RCfvsSc3gHa82ARd9z3HKKGKY_Qfl2IAft9eYzGTjAU61mVpZAt37GvBvGCAZTigQ1YuDuHg-asBHAJjzZkYXsJ6zoBi9eNHZ0tbAmeEzTp3Y-7feLLJRAFuH9FWCIU_CDgUKNTHRaC0pClBTNw4soCDdvLcK4zBfGo0CpXPNU8UYzyIshEDsjjFDXLeWnOENaRFYLTACcYiwwvhIY3mDBKDcuLgOsWYrWRpPJa4rakxZusSWOv0plWWtPKIJRg2hYim1kLp6Xxx_i0tr_8tiYkuPtfZ57_e-YN2umPhwM5eBg9XaBd2-OIYZeouVquzRVAkFV-XS2xT8dN2Ps |
| 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=Online+dictionary+learning+for+Local+Coordinate+Coding+with+Locality+Coding+Adaptors&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Pang%2C+Junbiao&rft.au=Zhang%2C+Chunjie&rft.au=Qin%2C+Lei&rft.au=Zhang%2C+Weigang&rft.date=2015-06-01&rft.pub=Elsevier+B.V&rft.issn=0925-2312&rft.eissn=1872-8286&rft.volume=157&rft.spage=61&rft.epage=69&rft_id=info:doi/10.1016%2Fj.neucom.2015.01.035&rft.externalDocID=S0925231215000569 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |