KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in ”double discriminant subspaces.” The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms

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  1. Wong, W. K.: Discover latent discriminant information for dimensionality reduction: non-negative sparseness preserving embedding (2012)
  2. Wu, Wei; Ahmad, M. Omair: A discriminant model for the pattern recognition of linearly independent samples (2012)
  3. Zhang, Yingwei; Zhang, Lingjun; Zhang, Hailong: Fault detection for industrial processes (2012)
  4. Zhang, Zhao; Zhao, Mingbo; Chow, Tommy W. S.: Constrained large margin local projection algorithms and extensions for multimodal dimensionality reduction (2012)
  5. Zhao, Mingbo; Chow, Tommy W. S.; Zhang, Zhao: Random walk-based fuzzy linear discriminant analysis for dimensionality reduction (2012) ioport
  6. Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W. S.: Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction (2012)
  7. An, Senjian; Liu, Wanquan; Venkatesh, Svetha; Yan, Hong: Unified formulation of linear discriminant analysis methods and optimal parameter selection (2011)
  8. Chu, Wen-Sheng; Chen, Ju-Chin; Lien, Jenn-Jier James: Kernel discriminant transformation for image set-based face recognition (2011)
  9. Geng, Cong; Jiang, Xudong: Face recognition based on the multi-scale local image structures (2011) ioport
  10. Han, Zhenjun; Jiao, Jianbin; Zhang, Baochang; Ye, Qixiang; Liu, Jianzhuang: Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR) (2011) ioport
  11. Heo, Gyeongyong; Gader, Paul: Robust kernel discriminant analysis using fuzzy memberships (2011)
  12. Song, Xiaoning; Yang, Jingyu; Wu, Xiaojun; Yang, Xibei: An optimal symmetrical null space criterion of Fisher discriminant for feature extraction and recognition (2011)
  13. Yang, Jian; Zhang, Lei; Yang, Jing-Yu; Zhang, David: From classifiers to discriminators: a nearest neighbor rule induced discriminant analysis (2011)
  14. Debruyne, Michiel; Hubert, Mia; Van Horebeek, Johan: Detecting influential observations in kernel PCA (2010)
  15. Debruyne, Michiel; Verdonck, Tim: Robust kernel principal component analysis and classification (2010)
  16. Harrison, Robert F.; Pasupa, Kitsuchart: A simple iterative algorithm for parsimonious binary kernel Fisher discrimination (2010) ioport
  17. Kao, Wen-Chung; Hsu, Ming-Chai; Yang, Yueh-Yiing: Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition (2010)
  18. Nikolopoulos, Spiros; Zafeiriou, Stafanos; Nikolaidis, Nikos; Pitas, Ioannis: Image replica detection system utilizing R-trees and linear discriminant analysis (2010)
  19. Rueda, Luis; Oommen, B. John; Henríquez, Claudio: Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes (2010)
  20. Wang, Xiaoming; Chung, Fu-Lai; Wang, Shitong: On minimum class locality preserving variance support vector machine (2010)