Abstract
Sparse representation based classifier (SRC) has been successfully applied in different pattern recognition tasks. Based on the analyses on SRC, we find that SRC is a kind of nearest subspace classifier. In this paper, a new feature extraction algorithm called discriminative principal subspaces alignment (DPSA) is developed according to the geometrical interpretations of SRC. Namely, DPSA aims to find a subspace wherein samples lie close to the hyperplanes spanned by the their homogenous samples and appear far away to the hyperplanes spanned by the their heterogenous samples. Different from the existing SRC-based feature algorithms, DPSA does not need the reconstruction coefficient vectors computed by SRC. Hence, DPSA is much more efficient than the SRC-based feature extraction algorithms. The face recognition experiments conducted on three benchmark face images databases (AR database, the extended Yale B database and CMU PIE) demonstrate the superiority of our DPSA algorithm.
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Wei, L. (2014). Optimal Subspace Learning for Sparse Representation Based Classifier via Discriminative Principal Subspaces Alignment. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_30
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DOI: https://doi.org/10.1007/978-3-319-11740-9_30
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