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...
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| Vydáno v: | Pattern recognition Ročník 124; s. 108378 |
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01.04.2022
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| ISSN: | 0031-3203, 1873-5142 |
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| 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. |
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| 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 |
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| 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 |
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| Keywords | Dimensionality reduction Feature learning Contrastive PCA PCA |
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| SubjectTerms | Contrastive PCA Dimensionality reduction Feature learning PCA |
| Title | cPCA++: An efficient method for contrastive feature learning |
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