Shudong Hou

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Canonical correlation analysis (CCA) and partial least squares (PLS) are always used as fusing two feature sets. How to extend them to fuse multiple features in a generalized way is still an unsolved problem. In this paper, we propose a novel feature fusion method called multiple component analysis (MCA). By constructing a higher-order tensor, all kinds of(More)
Canonical correlation analysis (CCA) aims at extracting statistically uncorrelated features via conjugate orthonormalization constraints of the projection directions. However, the formulated directions under conjugate orthonormalization are not reliable when the training samples are few and the covariance matrix has not been exactly estimated. Additionally,(More)
The partial least squares (PLS) regression is a novel multivariate data analysis method developed from practical applications in real word. In this paper, we first present two new PLS modeling methods (OPLS and COPLS) according to different constraints, and then discuss the two methods theoretically. Based on the idea of PLS model, a new face recognition(More)
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