Sparse nuclear norm two dimensional principal component analysis

Abstract

Feature extraction is an important way to improve the performance of image recognition. Compared to most of feature extraction methods, the 2-D principal component analysis (2-DPCA) better preserves the structural information of images since it is unnecessary to transform the small size image matrices into high dimensional vectors during the calculation process. In order to improve the robustness of 2-DPCA, nuclear norm-based 2-DPCA (N-2-DPCA) was proposed using nuclear norm as matrix distance measurement. However, 2-DPCA and N-2-DPCA lack the function of sparse feature extraction and selection. Thus, in this paper, we extend N-2-DPCA to sparse case, which is called SN-2-DPCA, for sparse subspace learning. To efficiently solve the model, an alternatively iterative algorithm will also be presented. The proposed SN-2-DPCA would be compared with some advanced 1-D and 2-D feature extraction methods using four well-known data sets. Experimental results indicate the competitive advantage of SN-2-DPCA.