2015/12/20 by Dan Wu, Jiasong Wu, Wu, Dan +10
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #Image and Video Stabilization #Machine Learning (cs.LG) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1512.06337
7 pages, 1 figure, 4 tables
arxiv created 2015/12/20 · openalex publication_date 2015/12/20 · arxiv updated 2015/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.