[1] Wu Dan, Wu Jiasong, , et al. Kernel principal component analysis networkfor image classification [J]. Journal of Southeast University (English Edition), 2015, 31 (4): 469-473. [doi:10.3969/j.issn.1003-7985.2015.04.007]

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

31

Issue:

2015 4

Page:

469-473

Research Field:

Computer Science and Engineering

Publishing date:

2015-12-30

Info

Title:

Kernel principal component analysis networkfor image classification

Author(s):

Wu Dan¹;  4;  Wu Jiasong¹;  2;  3;  4;  Zeng Rui¹;  4;  Jiang Longyu¹;  4Lotfi Senhadji²;  3;  4;  Shu Huazhong¹;  4

¹Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 210096, China
²Institut National de la Santé et de la Recherche Médicale U 1099, Rennes 35000, France
³Laboratoire Traitement du Signal et de l’Image, Université de Rennes 1, Rennes 35000, France
⁴Centre de Recherche en Information Biomédicale Sino-Français, Nanjing 210096, China

Keywords:

deep learning;  kernel principal component analysis net(KPCANet);  principal component analysis net(PCANet);  face recognition;  object recognition;  handwritten digit recognition

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2015.04.007

Abstract:

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 show that 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.

References:

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Memo

Memo:

Biographies: Wu Dan(1990—), female, graduate; Shu Huazhong(corresponding author), male, doctor, professor, shu.list@seu.edu.cn.
Foundation items: 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).
Citation: Wu Dan, Wu Jiasong, Zeng Rui, et al. Kernel principal component analysis network for image classification[J].Journal of Southeast University(English Edition), 2015, 31(4):469-473.[doi:10.3969/j.issn.1003-7985.2015.04.007]

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