cPCA++: An efficient method for contrastive feature learning
•In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data.•This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance compone...
Saved in:
| Published in: | Pattern recognition Vol. 124; p. 108378 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.04.2022
|
| Subjects: | |
| ISSN: | 0031-3203, 1873-5142 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data.•This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA.•By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset.•Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings.•However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient.
In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA. By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset. Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings. However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient. Several experiments were conducted in order to compare the proposed method to state-of-the-art methods. These experiments show that the proposed method achieves performance that is similar to or better than that of the other methods, while being more efficient. |
|---|---|
| AbstractList | •In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data.•This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA.•By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset.•Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings.•However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient.
In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA. By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset. Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings. However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient. Several experiments were conducted in order to compare the proposed method to state-of-the-art methods. These experiments show that the proposed method achieves performance that is similar to or better than that of the other methods, while being more efficient. |
| ArticleNumber | 108378 |
| Author | Kuo, C.-C. Jay Salloum, Ronald |
| Author_xml | – sequence: 1 givenname: Ronald surname: Salloum fullname: Salloum, Ronald email: Ronald.Salloum@csusb.edu organization: School of Computer Science and Engineering, California State University, San Bernardino, CA, 92407, United States – sequence: 2 givenname: C.-C. Jay surname: Kuo fullname: Kuo, C.-C. Jay email: jckuo@usc.edu organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, United States |
| BookMark | eNqFkEtLAzEUhYNUsK3-AxezL1Pz6iRTRCilPqCgC12HzM1NTWlnSiYW_PdOHVcudHXhXL4D5xuRQd3USMg1o1NGWXGznR5sgmYz5ZSzLtJC6TMyZFqJfMYkH5AhpYLlglNxQUZtu6WUqe4xJLfwslxMJvNsUWfofYCAdcr2mN4bl_kmZtDUKdo2hSNmHm36iJjt0MY61JtLcu7trsWrnzsmb_er1-Vjvn5-eFou1jnwQulcorV8BpICta50CiVHWXpuhWBlJUVVWOUqqStQKBxY1J5rwLJA77QAJ8ZE9r0Qm7aN6M0hhr2Nn4ZRczJgtqY3YE4GTG-gw-a_MAjJpvC9KOz-g-96GLthx4DRtCc3gC5EhGRcE_4u-AKvo3vp |
| CitedBy_id | crossref_primary_10_1016_j_patcog_2023_109342 crossref_primary_10_1080_10618600_2023_2289542 crossref_primary_10_1093_cercor_bhae393 crossref_primary_10_1016_j_patcog_2023_109739 crossref_primary_10_1016_j_patcog_2023_109692 |
| Cites_doi | 10.1007/s11042-016-3795-2 10.1038/s41467-018-04608-8 10.1186/s12864-019-6413-7 10.1186/1475-925X-14-S2-S6 10.1016/j.patcog.2015.01.024 10.1137/0707039 10.1007/s11263-013-0688-y 10.1109/5.726791 10.1038/ncomms14049 10.1111/1467-9868.00196 10.1016/j.jvcir.2018.01.010 10.21105/joss.00861 10.1016/j.patcog.2006.12.009 10.1016/j.patcog.2011.04.014 10.1007/s11263-015-0816-y 10.1016/j.imavis.2009.02.001 10.1016/j.patcog.2020.107346 |
| ContentType | Journal Article |
| Copyright | 2021 |
| Copyright_xml | – notice: 2021 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.patcog.2021.108378 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-5142 |
| ExternalDocumentID | 10_1016_j_patcog_2021_108378 S0031320321005586 |
| GroupedDBID | --K --M -D8 -DT -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 29O 4.4 457 4G. 53G 5VS 6I. 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFRF ABHFT ABJNI ABMAC ABTAH ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADMXK ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FD6 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM KZ1 LG9 LMP LY1 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SST SSV SSZ T5K TN5 UNMZH VOH WUQ XJE XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c2678-4eaa25c40c0ad9d7e42e49f2a3319b43b6a7db48bc7e3dcae8f28ce96efd83cd3 |
| ISSN | 0031-3203 |
| IngestDate | Sat Nov 29 07:25:16 EST 2025 Tue Nov 18 22:15:41 EST 2025 Fri Feb 23 02:39:32 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Dimensionality reduction Feature learning Contrastive PCA PCA |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c2678-4eaa25c40c0ad9d7e42e49f2a3319b43b6a7db48bc7e3dcae8f28ce96efd83cd3 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.patcog.2021.108378 |
| ParticipantIDs | crossref_primary_10_1016_j_patcog_2021_108378 crossref_citationtrail_10_1016_j_patcog_2021_108378 elsevier_sciencedirect_doi_10_1016_j_patcog_2021_108378 |
| PublicationCentury | 2000 |
| PublicationDate | April 2022 2022-04-00 |
| PublicationDateYYYYMMDD | 2022-04-01 |
| PublicationDate_xml | – month: 04 year: 2022 text: April 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Pattern recognition |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Duda, Hart, Stork (bib0001) 2001 Lecun, Bottou, Bengio, Haffner (bib0007) 1998; 86 Tipping, Bishop (bib0010) 1999; 61 Laub (bib0014) 2004 Peters, Wilkinson (bib0015) 1970; 7 Dong, Wang, Tan (bib0027) 2013 Zheng, Terry, Belgrader, Ryvkin, Bent, Wilson, Ziraldo, Wheeler, McDermott, Zhu, Gregory, Shuga, Montesclaros, Underwood, Masquelier, Nishimura, Schnall-Levin, Wyatt, Hindson, Bharadwaj, Wong, Ness, Beppu, Deeg, McFarland, Loeb, Valente, Ericson, Stevens, Radich, Mikkelsen, Hindson, Bielas (bib0024) 2017; 8 Salloum, Ren, Kuo (bib0020) 2018; 51 Dollár, Zitnick (bib0026) 2013 Wang, Wang, Hu, Yan (bib0004) 2015; 48 Zampoglou, Papadopoulos, Kompatsiaris (bib0025) 2017; 76 Ye, Sun, Chang (bib0030) 2007 Tsang, Kwong, Wang (bib0009) 2007; 40 SNE, 2020, (accessed July 21, 2020). Mahdian, Saic (bib0028) 2009; 27 Ronneberger, Fischer, Brox (bib0034) 2015 Dong, Moses, Li (bib0016) 2011 Banos, Villalonga, Garcia, Saez, Damas, Holgado-Terriza, Lee, Pomares, Rojas (bib0023) 2015; 14 Chicco, Jurman (bib0035) 2020; 21 Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein, Berg, Fei-Fei (bib0022) 2015; 115 Horn, Johnson (bib0037) 2012 van der Maaten, Hinton (bib0002) 2008; 9 L. van der Maaten Wang, Yan, Xu, Tang, Huang (bib0013) 2007 Kay (bib0011) 1993; vol. 2 Passalis, Raitoharju, Tefas, Gabbouj (bib0033) 2020; 105 Yan, Yuan, Yan, Yang (bib0003) 2011; 44 Defense Advanced Research Projects Agency, Media Forensics (MediFor), 2020, (accessed July 21, 2020). Parlett (bib0038) 1998 Forsythe (bib0012) 1997; 10 Hsu, Chang (bib0036) 2006 L. McInnes, UMAP, 2021, (accessed July 28, 2021). L. McInnes, J. Healy, J. Melville, UMAP: uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv J.D. Baron, R. Darling, K-nearest neighbor approximation via the friend-of-a-friend principle, arXiv preprint arXiv Lyu, Pan, Zhang (bib0029) 2014; 110 (2019). (2018). Abid, Zhang, Bagaria, Zou (bib0005) 2018; 9 Krizhevsky, Sutskever, Hinton (bib0031) 2012 Fukunaga (bib0008) 1990 Long, Shelhamer, Darrell (bib0032) 2015 Kay (10.1016/j.patcog.2021.108378_bib0011) 1993; vol. 2 10.1016/j.patcog.2021.108378_bib0019 Abid (10.1016/j.patcog.2021.108378_bib0005) 2018; 9 Wang (10.1016/j.patcog.2021.108378_bib0013) 2007 10.1016/j.patcog.2021.108378_bib0018 10.1016/j.patcog.2021.108378_bib0017 Peters (10.1016/j.patcog.2021.108378_bib0015) 1970; 7 Krizhevsky (10.1016/j.patcog.2021.108378_bib0031) 2012 Hsu (10.1016/j.patcog.2021.108378_bib0036) 2006 Dong (10.1016/j.patcog.2021.108378_bib0016) 2011 Salloum (10.1016/j.patcog.2021.108378_bib0020) 2018; 51 Parlett (10.1016/j.patcog.2021.108378_bib0038) 1998 Zheng (10.1016/j.patcog.2021.108378_bib0024) 2017; 8 Mahdian (10.1016/j.patcog.2021.108378_bib0028) 2009; 27 van der Maaten (10.1016/j.patcog.2021.108378_bib0002) 2008; 9 Long (10.1016/j.patcog.2021.108378_bib0032) 2015 Lecun (10.1016/j.patcog.2021.108378_bib0007) 1998; 86 Dong (10.1016/j.patcog.2021.108378_bib0027) 2013 Wang (10.1016/j.patcog.2021.108378_bib0004) 2015; 48 Russakovsky (10.1016/j.patcog.2021.108378_bib0022) 2015; 115 Passalis (10.1016/j.patcog.2021.108378_bib0033) 2020; 105 10.1016/j.patcog.2021.108378_bib0006 Dollár (10.1016/j.patcog.2021.108378_sbref0026) 2013 Yan (10.1016/j.patcog.2021.108378_bib0003) 2011; 44 Tsang (10.1016/j.patcog.2021.108378_bib0009) 2007; 40 Forsythe (10.1016/j.patcog.2021.108378_bib0012) 1997; 10 Ye (10.1016/j.patcog.2021.108378_bib0030) 2007 Banos (10.1016/j.patcog.2021.108378_bib0023) 2015; 14 10.1016/j.patcog.2021.108378_bib0021 Ronneberger (10.1016/j.patcog.2021.108378_bib0034) 2015 Zampoglou (10.1016/j.patcog.2021.108378_bib0025) 2017; 76 Tipping (10.1016/j.patcog.2021.108378_bib0010) 1999; 61 Laub (10.1016/j.patcog.2021.108378_bib0014) 2004 Chicco (10.1016/j.patcog.2021.108378_bib0035) 2020; 21 Lyu (10.1016/j.patcog.2021.108378_bib0029) 2014; 110 Horn (10.1016/j.patcog.2021.108378_bib0037) 2012 Fukunaga (10.1016/j.patcog.2021.108378_bib0008) 1990 Duda (10.1016/j.patcog.2021.108378_bib0001) 2001 |
| References_xml | – reference: (2018). – volume: 10 start-page: 99 year: 1997 end-page: 126 ident: bib0012 article-title: Utilizing waveform features for adaptive beamforming and direction finding with narrowband signals publication-title: Lincoln Lab. J. – volume: 27 start-page: 1497 year: 2009 end-page: 1503 ident: bib0028 article-title: Using noise inconsistencies for blind image forensics publication-title: Image Vis. Comput. – volume: 86 start-page: 2278 year: 1998 end-page: 2324 ident: bib0007 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE – year: 1990 ident: bib0008 article-title: Introduction to Statistical Pattern Recognition – start-page: 1841 year: 2013 end-page: 1848 ident: bib0026 article-title: Structured forests for fast edge detection publication-title: Proceedings of the 2013 IEEE International Conference on Computer Vision – reference: (2019). – volume: 115 start-page: 211 year: 2015 end-page: 252 ident: bib0022 article-title: ImageNet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. – start-page: 234 year: 2015 end-page: 241 ident: bib0034 article-title: U-Net: convolutional networks for biomedical image segmentation publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 61 start-page: 611 year: 1999 end-page: 622 ident: bib0010 article-title: Probabilistic principal component analysis publication-title: J. R. Stat. Soc. – reference: L. van der Maaten, – start-page: 422 year: 2013 end-page: 426 ident: bib0027 article-title: CASIA image tampering detection evaluation database publication-title: 2013 IEEE China Summit and International Conference on Signal and Information Processing – volume: 51 start-page: 201 year: 2018 end-page: 209 ident: bib0020 article-title: Image splicing localization using a multi-task fully convolutional network (MFCN) publication-title: J. Vis. Commun. Image Represent. – year: 2006 ident: bib0036 article-title: Detecting image splicing using geometry invariants and camera characteristics consistency publication-title: International Conference on Multimedia and Expo – volume: 105 start-page: 107346 year: 2020 ident: bib0033 article-title: Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits publication-title: Pattern Recognit. – start-page: 1097 year: 2012 end-page: 1105 ident: bib0031 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – year: 2012 ident: bib0037 article-title: Matrix Analysis – reference: J.D. Baron, R. Darling, K-nearest neighbor approximation via the friend-of-a-friend principle, arXiv preprint arXiv: – volume: 76 start-page: 4801 year: 2017 end-page: 4834 ident: bib0025 article-title: Large-scale evaluation of splicing localization algorithms for web images publication-title: Multimed. Tools Appl. – volume: 40 start-page: 2373 year: 2007 end-page: 2391 ident: bib0009 article-title: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection publication-title: Pattern Recognit. – reference: -SNE, 2020, (accessed July 21, 2020). – start-page: 577 year: 2011 end-page: 586 ident: bib0016 article-title: Efficient k-nearest neighbor graph construction for generic similarity measures publication-title: Proceedings of the 20th International conference on World Wide Web – volume: 21 year: 2020 ident: bib0035 article-title: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation publication-title: BMC Genomics – year: 2001 ident: bib0001 article-title: Pattern Classification – start-page: 1 year: 2007 end-page: 8 ident: bib0013 article-title: Trace ratio vs. ratio trace for dimensionality reduction publication-title: 2007 IEEE Conference on Computer Vision and Pattern Recognition – volume: 48 start-page: 3135 year: 2015 end-page: 3144 ident: bib0004 article-title: Visual data denoising with a unified Schatten-p norm and publication-title: Pattern Recognit. – start-page: 12 year: 2007 end-page: 15 ident: bib0030 article-title: Detecting digital image forgeries by measuring inconsistencies of blocking artifact publication-title: 2007 IEEE International Conference on Multimedia and Expo – year: 2004 ident: bib0014 article-title: Matrix Analysis For Scientists And Engineers – volume: 8 start-page: 1 year: 2017 end-page: 12 ident: bib0024 article-title: Massively parallel digital transcriptional profiling of single cells publication-title: Nat. Commun. – volume: 7 start-page: 479 year: 1970 end-page: 492 ident: bib0015 article-title: Ax= publication-title: SIAM J. Numer. Anal. – volume: 110 start-page: 202 year: 2014 end-page: 221 ident: bib0029 article-title: Exposing region splicing forgeries with blind local noise estimation publication-title: Int. J. Comput. Vis. – year: 1998 ident: bib0038 article-title: The Symmetric Eigenvalue Problem – volume: 44 start-page: 2834 year: 2011 end-page: 2842 ident: bib0003 article-title: Correntropy based feature selection using binary projection publication-title: Pattern Recognit. – volume: vol. 2 year: 1993 ident: bib0011 article-title: Fundamentals of statistical signal processing: detection theory publication-title: Fundamentals of Statistical Signal Processing – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: bib0002 article-title: Visualizing high-dimensional data using publication-title: J. Mach. Learn. Res. – reference: L. McInnes, UMAP, 2021, (accessed July 28, 2021). – reference: L. McInnes, J. Healy, J. Melville, UMAP: uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv: – reference: Defense Advanced Research Projects Agency, Media Forensics (MediFor), 2020, (accessed July 21, 2020). – volume: 9 start-page: 7 year: 2018 ident: bib0005 article-title: Exploring patterns enriched in a dataset with contrastive principal component analysis publication-title: Nat. Commun. – start-page: 3431 year: 2015 end-page: 3440 ident: bib0032 article-title: Fully convolutional networks for semantic segmentation publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 14 year: 2015 ident: bib0023 article-title: Design, implementation and validation of a novel open framework for agile development of mobile health applications publication-title: Biomed. Eng. Online – year: 1990 ident: 10.1016/j.patcog.2021.108378_bib0008 – year: 2001 ident: 10.1016/j.patcog.2021.108378_bib0001 – start-page: 1097 year: 2012 ident: 10.1016/j.patcog.2021.108378_bib0031 article-title: ImageNet classification with deep convolutional neural networks – volume: 76 start-page: 4801 issue: 4 year: 2017 ident: 10.1016/j.patcog.2021.108378_bib0025 article-title: Large-scale evaluation of splicing localization algorithms for web images publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-016-3795-2 – year: 2006 ident: 10.1016/j.patcog.2021.108378_bib0036 article-title: Detecting image splicing using geometry invariants and camera characteristics consistency – volume: 9 start-page: 7 issue: 1 year: 2018 ident: 10.1016/j.patcog.2021.108378_bib0005 article-title: Exploring patterns enriched in a dataset with contrastive principal component analysis publication-title: Nat. Commun. doi: 10.1038/s41467-018-04608-8 – volume: 21 issue: 1 year: 2020 ident: 10.1016/j.patcog.2021.108378_bib0035 article-title: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation publication-title: BMC Genomics doi: 10.1186/s12864-019-6413-7 – year: 2004 ident: 10.1016/j.patcog.2021.108378_bib0014 – year: 1998 ident: 10.1016/j.patcog.2021.108378_bib0038 – volume: 10 start-page: 99 issue: 2 year: 1997 ident: 10.1016/j.patcog.2021.108378_bib0012 article-title: Utilizing waveform features for adaptive beamforming and direction finding with narrowband signals publication-title: Lincoln Lab. J. – start-page: 422 year: 2013 ident: 10.1016/j.patcog.2021.108378_bib0027 article-title: CASIA image tampering detection evaluation database – volume: 14 issue: Suppl 2 year: 2015 ident: 10.1016/j.patcog.2021.108378_bib0023 article-title: Design, implementation and validation of a novel open framework for agile development of mobile health applications publication-title: Biomed. Eng. Online doi: 10.1186/1475-925X-14-S2-S6 – volume: 48 start-page: 3135 issue: 10 year: 2015 ident: 10.1016/j.patcog.2021.108378_bib0004 article-title: Visual data denoising with a unified Schatten-p norm and ℓq norm regularized principal component pursuit publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2015.01.024 – volume: 7 start-page: 479 issue: 4 year: 1970 ident: 10.1016/j.patcog.2021.108378_bib0015 article-title: Ax=λBx and the generalized eigenproblem publication-title: SIAM J. Numer. Anal. doi: 10.1137/0707039 – ident: 10.1016/j.patcog.2021.108378_bib0018 – start-page: 1841 year: 2013 ident: 10.1016/j.patcog.2021.108378_sbref0026 article-title: Structured forests for fast edge detection – volume: vol. 2 year: 1993 ident: 10.1016/j.patcog.2021.108378_bib0011 article-title: Fundamentals of statistical signal processing: detection theory – volume: 110 start-page: 202 issue: 2 year: 2014 ident: 10.1016/j.patcog.2021.108378_bib0029 article-title: Exposing region splicing forgeries with blind local noise estimation publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-013-0688-y – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 10.1016/j.patcog.2021.108378_bib0007 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 – start-page: 12 year: 2007 ident: 10.1016/j.patcog.2021.108378_bib0030 article-title: Detecting digital image forgeries by measuring inconsistencies of blocking artifact – ident: 10.1016/j.patcog.2021.108378_bib0021 – volume: 8 start-page: 1 year: 2017 ident: 10.1016/j.patcog.2021.108378_bib0024 article-title: Massively parallel digital transcriptional profiling of single cells publication-title: Nat. Commun. doi: 10.1038/ncomms14049 – volume: 61 start-page: 611 issue: 3 year: 1999 ident: 10.1016/j.patcog.2021.108378_bib0010 article-title: Probabilistic principal component analysis publication-title: J. R. Stat. Soc. doi: 10.1111/1467-9868.00196 – volume: 51 start-page: 201 year: 2018 ident: 10.1016/j.patcog.2021.108378_bib0020 article-title: Image splicing localization using a multi-task fully convolutional network (MFCN) publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2018.01.010 – year: 2012 ident: 10.1016/j.patcog.2021.108378_bib0037 – volume: 9 start-page: 2579 year: 2008 ident: 10.1016/j.patcog.2021.108378_bib0002 article-title: Visualizing high-dimensional data using t-SNE publication-title: J. Mach. Learn. Res. – start-page: 1 year: 2007 ident: 10.1016/j.patcog.2021.108378_bib0013 article-title: Trace ratio vs. ratio trace for dimensionality reduction – ident: 10.1016/j.patcog.2021.108378_bib0006 doi: 10.21105/joss.00861 – volume: 40 start-page: 2373 issue: 9 year: 2007 ident: 10.1016/j.patcog.2021.108378_bib0009 article-title: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2006.12.009 – start-page: 577 year: 2011 ident: 10.1016/j.patcog.2021.108378_bib0016 article-title: Efficient k-nearest neighbor graph construction for generic similarity measures – ident: 10.1016/j.patcog.2021.108378_bib0019 – volume: 44 start-page: 2834 issue: 12 year: 2011 ident: 10.1016/j.patcog.2021.108378_bib0003 article-title: Correntropy based feature selection using binary projection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2011.04.014 – start-page: 234 year: 2015 ident: 10.1016/j.patcog.2021.108378_bib0034 article-title: U-Net: convolutional networks for biomedical image segmentation – ident: 10.1016/j.patcog.2021.108378_bib0017 – start-page: 3431 year: 2015 ident: 10.1016/j.patcog.2021.108378_bib0032 article-title: Fully convolutional networks for semantic segmentation – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 10.1016/j.patcog.2021.108378_bib0022 article-title: ImageNet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-015-0816-y – volume: 27 start-page: 1497 issue: 10 year: 2009 ident: 10.1016/j.patcog.2021.108378_bib0028 article-title: Using noise inconsistencies for blind image forensics publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2009.02.001 – volume: 105 start-page: 107346 year: 2020 ident: 10.1016/j.patcog.2021.108378_bib0033 article-title: Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107346 |
| SSID | ssj0017142 |
| Score | 2.4015682 |
| Snippet | •In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data.•This technique,... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108378 |
| SubjectTerms | Contrastive PCA Dimensionality reduction Feature learning PCA |
| Title | cPCA++: An efficient method for contrastive feature learning |
| URI | https://dx.doi.org/10.1016/j.patcog.2021.108378 |
| Volume | 124 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-5142 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017142 issn: 0031-3203 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NS-QwFA_r6MGLuh_i55LD7mnI0CbppBEvs4OL60EG1oW5lTRJRZE6OCr--b58tB13RdeDUEIpbVryC68v7738fgh9y2xmEkkFUZIpwoXOiGTGEDXMeJUKK63QXmxCnJ7m06mcROG_uZcTEHWdPzzI2btCDdcAbLd19g1wt53CBTgH0KEF2KH9L-D1ZDz6Tn-4IwT9rGeJcDn_IBftKwt9ibqaB9Zv69k9GwWJ80WHdeL5N92el1ho1KXtf7ucfZRJ9hHmLi0U4q8DMh70T2KVTowswKK0K0iJ1pKlhNGEPbGWlC_YuzRxhPTPmuIQFbgczOCXcn0OK3GaDrrbnzJf__VHausEmxK0yyL0UrheitDLElqmIpN5Dy2Pfh1NT9rckUh54IiPX99smPRVff9-zfMOyYKTcbaB1uLqAI8Cqh_RB1t_QuuN8gaOhvgzOnQg9_sHeFTjFl4c4MUAL16AF0d4cQPvF_Tn59HZ-JhEHQyiKfgShFulaKZ5ohNlpBGWU8tlRRUD-1lyVg6VMCXPSy0sM1rZvKK5tnJoK5Mzbdgm6tXXtd1CeMgqm0gFXlnJeCXTMpOmSkshFDjKFZXbiDWDUehIEu-0Sq6Kl6DYRqR9ahZIUl65XzTjXERHLzhwBUyeF5_ceeObdtFqN7P3UO_25s7uoxV9f3sxv_kaZ84jY5tyrg |
| linkProvider | Elsevier |
| 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=cPCA%2B%2B%3A+An+efficient+method+for+contrastive+feature+learning&rft.jtitle=Pattern+recognition&rft.au=Salloum%2C+Ronald&rft.au=Kuo%2C+C.-C.+Jay&rft.date=2022-04-01&rft.issn=0031-3203&rft.volume=124&rft.spage=108378&rft_id=info:doi/10.1016%2Fj.patcog.2021.108378&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_patcog_2021_108378 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0031-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0031-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0031-3203&client=summon |