Name
Abstract | ||
|---|---|---|
The extraction of effective features is extremely important for understanding the intrinsic structure hidden in high-dimensional data. In recent years, sparse representation models have been widely used in feature extraction. A supervised learning method, called sparsity preserving discriminative learning (SPDL), is proposed. SPDL, which attempts to preserve the sparse representation structure of the data and simultaneously maximize the between-class separability, can be regarded as a combiner of manifold learning and sparse representation. More specifically, SPDL first creates a concatenated dictionary by class-wise principal component analysis decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least squares method. Second, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds. Then, SPDL integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experimental results on several popular face databases demonstrate the effectiveness of the proposed approach. (C) 2016 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/1.JEI.25.1.013005 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
feature extraction,sparse representation,class-wise principal component analysis decompositions,manifold learning | Facial recognition system,Pattern recognition,Computer science,Sparse approximation,Supervised learning,Feature extraction,Associative array,Artificial intelligence,Concatenation,Nonlinear dimensionality reduction,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
25 | 1 | 1017-9909 |
Citations | PageRank | References |
1 | 0.39 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yingchun ren | 1 | 1 | 0.39 |
| Zhicheng Wang | 2 | 176 | 17.00 |
| Yufei Chen | 3 | 322 | 33.06 |
| xiaoying shan | 4 | 1 | 0.39 |
| weidong zhao | 5 | 77 | 14.73 |