15 November 2007 Classification method based on KCCA
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Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880W (2007) https://doi.org/10.1117/12.774688
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
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.
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Zhanqing Wang, Zhanqing Wang, Guilin Zhang, Guilin Zhang, Guangzhou Zhao, Guangzhou Zhao, } "Classification method based on KCCA", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880W (15 November 2007); doi: 10.1117/12.774688; https://doi.org/10.1117/12.774688
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