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	Tensor canonical correlation analysis for multi-view dimension reduction
	Luo, Yong ; Tao, Dacheng ; Ramamohanarao, Kotagiri ; Xu, Chao ; Wen, Yonggang
	2016
英文摘要	Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multiview canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. ? 2016 IEEE.; EI; 1460-1461
语种	英语
出处	32nd IEEE International Conference on Data Engineering, ICDE 2016
DOI标识	10.1109/ICDE.2016.7498374
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449442]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Luo, Yong,Tao, Dacheng,Ramamohanarao, Kotagiri,et al. Tensor canonical correlation analysis for multi-view dimension reduction. 2016-01-01.

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