Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction
Document Type
Article
Date of Original Version
1-1-2014
Abstract
Sparse-representation-based classification (SRC), which classifies data based on the sparse reconstruction error, has been a new technique in pattern recognition. However, the computation cost for sparse coding is heavy in real applications. In this paper, various dimension reduction methods are studied in the context of SRC to improve classification accuracy as well as reduce computational cost. A feature extraction method, i.e., principal component analysis, and feature selection methods, i.e., Laplacian score and Pearson correlation coefficient, are applied to the data preparation step to preserve the structure of data in the lower-dimensional space. Classification performance of SRC with structure-preserving dimension reduction (SRC-SPDR) is compared to classical classifiers such as k-nearest neighbors and support vector machines. Experimental tests with the UCI and face data sets demonstrate that SRC-SPDR is effective with relatively low computation cost © 2014 Springer Science+Business Media New York.
Publication Title, e.g., Journal
Cognitive Computation
Volume
6
Issue
3
Citation/Publisher Attribution
Xu, Jin, Guang Yang, Yafeng Yin, Hong Man, and Haibo He. "Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction." Cognitive Computation 6, 3 (2014): 608-621. doi: 10.1007/s12559-014-9252-5.