Kernel principal component analysis network for image classification release_qnwldiyk3fe37hb7rpxoyiqxim

by Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong Shu

Released as a article .

2015  

Abstract

In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed. First, mapping the data into higher space with kernel principal component analysis to make the data linearly separable. Then building a two-layer KPCANet to obtain the principal components of image. Finally, classifying the principal components with linearly classifier. Experimental results show that the proposed KPCANet is effective in face recognition, object recognition and hand-writing digits recognition, it also outperforms principal component analysis network (PCANet) generally as well. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
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Date   2015-12-20
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arXiv  1512.06337v1
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