Feature extraction using graph discriminant embedding

Marginal fisher analysis (MFA) is an effective approach for feature extraction and recognition. However, an intrinsic limitation existed in MFA is that it deemphasizes the importance of the distant points, which may degrade the recognition performance. In this paper, a novel algorithm called graph discriminant embedding (GDE) is proposed to overcome the limitation. GDE maintains the good property of MFA and emphasizes the importance of the distant points as well as that of the nearby points, seeking to find a set of optimal directions to maximize the inter-class scatter and simultaneously minimize the intra-class scatter. Experimental results on the ORL and Yale face databases show the effectiveness of the proposed algorithm.

[1]  Jingjing Liu,et al.  Two-dimensional margin, similarity and variation embedding , 2012, Neurocomputing.

[2]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

[4]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Xiaolong Teng,et al.  Face recognition using discriminant locality preserving projections , 2006, Image Vis. Comput..

[6]  Zhenmin Tang,et al.  Nearest-neighbor classifier motivated marginal discriminant projections for face recognition , 2011, Frontiers of Computer Science in China.

[7]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[8]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[9]  Jian Yang,et al.  Sparse margin based discriminant analysis for face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[10]  Caikou Chen,et al.  Enhanced Marginal Fisher Analysis for Face Recognition , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[11]  Zhongliang Jing,et al.  Local structure based supervised feature extraction , 2006, Pattern Recognit..

[12]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Zhihui Lai,et al.  Fuzzy local maximal marginal embedding for feature extraction , 2012, Soft Comput..

[14]  Zhihui Lai,et al.  Local Maximal Marginal Embedding with Application to Face Recognition , 2008, 2008 Chinese Conference on Pattern Recognition.

[15]  Zhong Jin,et al.  Feature Extraction Based on Maximum Nearest Subspace Margin Criterion , 2013, Neural Processing Letters.