http://dx.doi.org/10.1109/TPAMI.2013.104">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Sparse canonical correlation analysis: New formulation and algorithm

Language

English

Abstract

In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms. © 2013 IEEE.

Keywords

canonical correlation analysis, linear discriminant analysis, multivariate data, orthogonality, Sparsity

Publication Date

2013

Source Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

35

Issue

12

Start Page

3050

End Page

3065

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

01628828

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