Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Symmetric deflation based multiview canonical correlation learning algorithm
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 eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively and stably solving higherstage 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.
Symmetric deflation based multiview canonical correlation learning algorithm
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 eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively and stably solving higherstage 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.
Symmetric deflation based multiview canonical correlation learning algorithm
Yuan, Yun-Hao (Autor:in) / Li, Yun (Autor:in) / Li, Chaofeng (Autor:in) / Li, Bin (Autor:in) / Qiang, Jipeng (Autor:in) / Fang, Wei (Autor:in) / Wu, Xiaojun (Autor:in) / Sun, Quansen (Autor:in)
01.09.2017
292040 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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