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Proceedings Paper

Classification method based on KCCA
Author(s): Zhanqing Wang; Guilin Zhang; Guangzhou Zhao
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Paper Abstract

Nonlinear CCA extends the linear CCA in that it operates in the kernel space and thus implies the nonlinear combinations in the original space. This paper presents a classification method based on the kernel canonical correlation analysis (KCCA). We introduce the probabilistic label vectors (PLV) for a give pattern which extend the conventional concept of class label, and investigate the correlation between feature variables and PLV variables. A PLV predictor is presented based on KCCA, and then classification is performed on the predicted PLV. We formulate a frame for classification by integrating class information through PLV. Experimental results on Iris data set classification and facial expression recognition show the efficiencies of the proposed method.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880W (15 November 2007); doi: 10.1117/12.774688
Show Author Affiliations
Zhanqing Wang, Huazhong Univ. of Science and Technology (China)
Wuhan Univ. of Technology (China)
Guilin Zhang, Huazhong Univ. of Science and Technology (China)
Guangzhou Zhao, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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