Feature Extraction Based on Maximum Nearest Subspace Margin Criterion
Feature Extraction Based on Maximum Nearest Subspace Margin Criterion
复制标题
基于最大最近子空间裕度准则的特征提取
DOI：
10.1007/s11063-012-9252-y
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发表时间：
2013-06-01
影响因子：
3.1
通讯作者：
Jin, Zhong
中科院分区：
文献类型：
--
作者：
Chen, Yi;Li, Zhenzhen;Jin, Zhong
基于稀疏表示分类（SRC）和线性回归分类（LRC）的分类规则，我们提出了用于特征提取的最大最近子空间边缘准则。所提方法可被视为SRC和LRC的预处理步骤。通过同时最大化类间重构误差和最小化类内重构误差，所提方法显著提高了SRC和LRC的性能。与线性判别分析相比，所提方法避免了小样本量问题，并且能够提取更多的特征。此外，我们对LRC进行了扩展以克服潜在的奇异问题。在扩展的耶鲁B（YALE - B）、AR、香港理工大学指节纹以及CENPARMI手写数字数据库上的实验结果证明了所提方法的有效性。
Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.