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Semi-supervised two phase test sample sparse representation classifier
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DOI：10.1016/j.knosys.2018.06.018.png)
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Abstract
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Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an efficient alternative to the Sparse Representation Classifier (SRC). It aims at classifying data using sparse coding in two phases with h regularization. Although high performances can be obtained by the TPTSSR classifier, since it is a supervised classifier, it is not able to benefit from unlabeled samples which are very often available. In this paper, we introduce a semi-supervised version of the TPTSSR classifier called Semi-supervised Two Phase Test Sample Sparse Representation (STPTSSR). STPTSSR combines the merits of sparse coding, active learning and the two phase collaborative representation classifiers. The proposed framework is able to make any sparse representation based classifier semi-supervised. Extensive experiments carried out on six benchmark image datasets show that the proposed STPTSSR can outperform the classical TPTSSR as well as many state-of-the-art semi-supervised methods.
Keywords:
Semi-supervised learning
Active learning
Sparse coding
Two phase test sample representation classifiers
Pattern classification
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