文献类型：期刊文章

作　　者：吴丹[1,2] 伍家松[1,3,4,2] 曾瑞[1,2] 姜龙玉[1,2] Lotfi Senhadji[3,5,2] 舒华忠[1,2]

机构地区：[1]东南大学计算机网络和信息集成教育部重点实验室,南京210096 [2]中法生物医学信息研究中心,南京210096 [3]Institut National de la Santé et de la Recherche Médicale U 1099 [4]Laboratoire Traitement du Signal et de l'Image Universitéde Rennes 1 [5]Laboratoire Traitement du Signal et de l'Image Université de Rennes 1

出　　处：《Journal of Southeast University(English Edition)》

基　　金：The National Natural Science Foundation of China(No.61201344;61271312;61401085;11301074);the Research Fund for the Doctoral Program of Higher Education(No.20120092120036);the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031);Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11);"333"Project(No.BRA2015288);High-End Foreign Experts Recruitment Program(No.GDT20153200043);Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)

年　　份：2015

卷　　号：31

期　　号：4

起止页码：469-473

语　　种：中文

收录情况：CAS、CSA、CSA-PROQEUST、EI(收录号：20160801961776)、IC、INSPEC、JST、MR、SCOPUS、ZMATH、普通刊

摘　　要：In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.

关 键 词：deep learning  kernel principal component analysis net（KPCANet）  principal component analysis net（PCANet）  face recognition  object recognition  handwritten digit recognition  

分 类 号：TP391]