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| Fuzzy Discriminant Analysis Based on Sparse Similarity Measurement |
| SONG Xiao Ning1,2,3,XU Yong4 |
1.School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003
2.School of Internet of Things Engineering,Jiangnan University,Wuxi 214122
3.School of Computer Science and Technology,Nanjing University of Science & Technology,Nanjing 210094
4.Bio Computing Research Center,Shenzhen Graduate School,Harbin Institute of Technology,Shenzhen 518055 |
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Abstract The mathematic essence of sparse representation is signal decomposition under the constraint of sparsity regularization. A fuzzy discriminant analysis based on sparse similarity measurement is proposed in this paper. Each high dimensional image sample is firstly partitioned into several local blocks with equal size by the proposed algorithm,and these local blocks are combined to represent the samples as a Ridgelet sequence. Then,a new sparse learning algorithm is presented for coefficient decomposition and the corresponding sparse similarity measurement,and the fuzzy discriminant analysis criterion is subsequently developed by embedding the sparse similarity. The proposed algorithm successfully utilizes the novel sparse supervised learning algorithm as a feature extraction tool. Meanwhile ,it overcomes the shortcomings of traditional discriminant analysis method derived from the lack of structure knowledge between samples,especially in the case of high dimensional nonlinear small sample sizes. The experimental results on the ORL and FERET face images show the effectiveness of the proposed method.
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Received: 06 December 2012
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2014, Vol. 27 
