Symmetric Deflation Based Multiview Canonical Correlation Learning Algorithm

摘要

Multiview canonical correlation analysis (MCCA) is an effective tool for analyzing the relationships among group-aligned multidimensional samples, which has been applied to the fields of pattern recognition and computer vision. In MCCA, its first-stage canonical variables are solved by a multivariate eigen-value problem that can be computed by Horst method. However, how to use the algorithm for effectively and stably solving higher-stage projection directions is now still not clear. In this paper, we propose a multiview canonical variates learning algorithm, which uses a symmetric deflation strategy instead of asymmetric one for multi-set solutions. Also, we prove the convergence property of the proposed algorithm. Clearly this benefits the theoretical development of MCCA and can facilitate its applications in practice.