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. Zhou, Yang; Chen, Di-Rong; Huang, Wei: A class of optimal estimators for the covariance operator in reproducing kernel Hilbert spaces (2019)
  2. Zhu, Ruifeng; Dornaika, Fadi; Ruichek, Yassine: Learning a discriminant graph-based embedding with feature selection for image categorization (2019)
  3. Zhang, Di; Li, Xueqiang; He, Jiazhong; Du, Minghui: A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection (2018)
  4. Ren, Yingchun; Wang, Zhicheng; Chen, Yufei; Zhao, Weidong: Sparsity preserving discriminant projections with applications to face recognition (2016)
  5. Wang, Zhen; Shao, Yuan-Hai; Bai, Lan; Li, Chun-Na; Liu, Li-Ming; Deng, Nai-Yang: MBLDA: a novel multiple between-class linear discriminant analysis (2016)
  6. Zhao, Mingbo; Chow, Tommy W. S.; Wu, Zhou; Zhang, Zhao; Li, Bing: Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction (2015)
  7. Yuan, Yun-Hao; Sun, Quan-Sen; Ge, Hong-Wei: Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition (2014)
  8. Zhang, Di; He, Jiazhong; Zhao, Yun; Luo, Zhongliang; Du, Minghui: Global plus local: a complete framework for feature extraction and recognition (2014)
  9. Zhang, Zhao; Yan, Shuicheng; Zhao, Mingbo: Similarity preserving low-rank representation for enhanced data representation and effective subspace learning (2014)
  10. Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W. S.; Li, Bing: A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction (2014)
  11. Zhu, Lin; Huang, De-Shuang: A Rayleigh-Ritz style method for large-scale discriminant analysis (2014)
  12. Gao, Jian-Qiang; Fan, Li-Ya; Li, Li; Xu, Li-Zhong: A practical application of kernel-based fuzzy discriminant analysis (2013)
  13. Song, Xiaoning; Liu, Zi; Yang, Xibei; Yang, Jingyu: A fuzzy supervised learning method with dynamical parameter estimation for nonlinear discriminant analysis (2013)
  14. Cui, Yan; Fan, Liya: A novel supervised dimensionality reduction algorithm: graph-based Fisher analysis (2012)
  15. Jing, Xiaoyuan; Li, Sheng; Zhang, David; Lan, Chao; Yang, Jingyu: Optimal subset-division based discrimination and its kernelization for face and palmprint recognition (2012)
  16. Kwak, Nojun: Kernel discriminant analysis for regression problems (2012)
  17. Lu, Gui-Fu; Zou, Jian; Wang, Yong: Incremental complete LDA for face recognition (2012)
  18. Sun, Zhongxi; Sun, Changyin; Yang, Wankou; Shen, Jifeng: Feature extraction using 2DIFDA with fuzzy membership (2012) ioport
  19. Wang, Jinghua; You, Jane; Li, Qin; Xu, Yong: Orthogonal discriminant vector for face recognition across pose (2012)
  20. Wang, Jinghua; You, Jane; Li, Qin; Xu, Yong: Extract minimum positive and maximum negative features for imbalanced binary classification (2012) ioport

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