TY - JOUR
T1 - Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction
AU - Xu, Jin
AU - Yang, Guang
AU - Yin, Yafeng
AU - Man, Hong
AU - He, Haibo
PY - 2014/9
Y1 - 2014/9
N2 - 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
AB - 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
KW - Classification
KW - Dimension reduction
KW - Feature extraction
KW - Feature selection
KW - Sparse representation (coding)
KW - Structure preserving
UR - http://www.scopus.com/inward/record.url?scp=84906948263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906948263&partnerID=8YFLogxK
U2 - 10.1007/s12559-014-9252-5
DO - 10.1007/s12559-014-9252-5
M3 - Article
AN - SCOPUS:84906948263
SN - 1866-9956
VL - 6
SP - 608
EP - 621
JO - Cognitive Computation
JF - Cognitive Computation
IS - 3
ER -