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Canonical random correlation analysis

Published: 22 March 2010 Publication History

Abstract

Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take class label information into account. In this paper, we introduce the within-class cross correlation into CCA and propose a new method called canonical Random Correlation Analysis (RCA). In RCA, besides considering the correlation between two views from the same sample, the cross correlations between two views respectively from different within-class samples are also used to achieve good performance. Two approaches for randomly generating cross correlation samples are developed.

References

[1]
P. Bourke. Cross correlation. http://local.wasp.uwa.edu.au/~pbourke/miscellaneous/correlate/, 1996.
[2]
D. R. Hardoon, S. Szedmák, and J. Shawe-Taylor. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 16(12):2639--2664, 2004.
[3]
T. Sun and S. Chen. Enhanced canonical correlation analysis with applications. Dissertation in Nanjing University of Aeronautics and Astronautics, 2006.
[4]
J. A. Wegelin. A survey of partial least squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, 2000.

Cited By

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  • (2023)Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight CorrectionArtificial Intelligence Logic and Applications10.1007/978-981-99-7869-4_35(428-436)Online publication date: 15-Nov-2023

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cover image ACM Conferences
SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
March 2010
2712 pages
ISBN:9781605586397
DOI:10.1145/1774088
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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Author Tags

  1. canonical correlation analysis
  2. dimensionality reduction
  3. discriminant
  4. multi-view

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SAC'10
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SAC'10: The 2010 ACM Symposium on Applied Computing
March 22 - 26, 2010
Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2023)Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight CorrectionArtificial Intelligence Logic and Applications10.1007/978-981-99-7869-4_35(428-436)Online publication date: 15-Nov-2023

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