A generalized subspace projection approach for sparse representation classification

Authors:
Bingxin Xu;Ping Guo
Affiliations:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China;Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

In this paper, we propose a subspace projection approach for sparse representation classification (SRC), which is based on Principal Component Analysis (PCA) and Maximal Linearly Independent Set (MLIS). In the projected subspace, each new vector of this space can be represented by a linear combination of MLIS. Substantial experiments on Scene15 and CalTech101 image datasets have been conducted to investigate the performance of proposed approach in multi-class image classification. The statistical results show that using proposed subspace projection approach in SRC can reach higher efficiency and accuracy.