Kernel Reverse Neighborhood Discriminant Analysis

被引:1
作者
Li, Wangwang [1 ]
Tan, Hengliang [1 ]
Feng, Jianwei [1 ]
Xie, Ming [1 ]
Du, Jiao [1 ]
Yang, Shuo [1 ]
Yan, Guofeng [1 ]
机构
[1] Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
linear discriminant analysis; kernel trick; reverse nearest neighbors; Gaussian kernel; LDA; EXTRACTION;
D O I
10.3390/electronics12061322
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Currently, neighborhood linear discriminant analysis (nLDA) exploits reverse nearest neighbors (RNN) to avoid the assumption of linear discriminant analysis (LDA) that all samples from the same class should be independently and identically distributed (i.i.d.). nLDA performs well when a dataset contains multimodal classes. However, in complex pattern recognition tasks, such as visual classification, the complex appearance variations caused by deformation, illumination and visual angle often generate non-linearity. Furthermore, it is not easy to separate the multimodal classes in lower-dimensional feature space. One solution to these problems is to map the feature to a higher-dimensional feature space for discriminant learning. Hence, in this paper, we employ kernel functions to map the original data to a higher-dimensional feature space, where the nonlinear multimodal classes can be better classified. We give the details of the deduction of the proposed kernel reverse neighborhood discriminant analysis (KRNDA) with the kernel tricks. The proposed KRNDA outperforms the original nLDA on most datasets of the UCI benchmark database. In high-dimensional visual recognition tasks of handwritten digit recognition, object categorization and face recognition, our KRNDA achieves the best recognition results compared to several sophisticated LDA-based discriminators.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Linear Discriminant Analysis and Kernel Vector Quantization for Mandarin Digits Recognition
    赵军辉
    谢湘
    匡镜明
    Journal of Beijing Institute of Technology(English Edition), 2004, (04) : 385 - 388
  • [32] Linear discriminant analysis with generalized kernel constraint for robust image classification
    Li, Shuyi
    Zhang, Hengmin
    Ma, Ruijun
    Zhou, Jianhang
    Wen, Jie
    Zhang, Bob
    PATTERN RECOGNITION, 2023, 136
  • [33] An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments
    Xu, Y
    Yang, JY
    Lu, JF
    Yu, DJ
    PATTERN RECOGNITION, 2004, 37 (10) : 2091 - 2094
  • [34] Robust kernel discriminant analysis and its application to feature extraction and recognition
    Liang, ZZ
    Zhang, D
    Shi, PF
    NEUROCOMPUTING, 2006, 69 (7-9) : 928 - 933
  • [35] Kernel Discriminant Learning for Ordinal Regression
    Sun, Bing-Yu
    Li, Jiuyong
    Wu, Desheng Dash
    Zhang, Xiao-Ming
    Li, Wen-Bo
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2010, 22 (06) : 906 - 910
  • [36] Kernel Discriminant Embedding in face recognition
    Han, Pang Ying
    Jin, Andrew Teoh Beng
    Ann, Toh Kar
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2011, 22 (07) : 634 - 642
  • [37] Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition
    Park, CH
    Park, H
    SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2005, 27 (01) : 87 - 102
  • [38] Kernel-based improved discriminant analysis and its application to face recognition
    Zhou, Dake
    Tang, Zhenmin
    SOFT COMPUTING, 2010, 14 (02) : 103 - 111
  • [39] Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches
    Cardoso, Jaime S.
    Sousa, Ricardo
    Domingues, Ines
    2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1, 2012, : 473 - 477
  • [40] Linear and Kernel Fisher Discriminant Analysis for studying diffusion tensor images in schizophrenia
    Vos, F. M.
    Caan, M. W. A.
    Vermeer, K. A.
    Majoie, C. B. L. M.
    den Heeten, G. J.
    van Vliet, L. J.
    2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3, 2007, : 764 - +