Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition

Ruicong ZHI  Lei ZHAO  Bolin SHI  Yi JIN  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.9   pp.2434-2442
Publication Date: 2014/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2013EDP7422
Type of Manuscript: PAPER
Category: Pattern Recognition
fuzzy assignment,  discriminant objective function,  locality preserving,  pattern recognition,  

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A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.