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New semi-supervised classification using a multi-modal feature joint L21-norm based sparse representation
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DOI：10.1016/j.image.2018.03.005.png)
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摘要
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In this paper, a novel semi-supervised classification method, namely sparse semi-supervised classification algorithm (SSSC) is proposed. To improve the reliability of SSSC, this study extends SSSC to multi-modal features joint L-21-norm based sparse representation. In the SSSC framework, the labeled patterns are sparsely represented by the abundance of unlabeled patterns, and then the scores of the unlabeled patterns are computed corresponding to the object class based on the relational degree vector. A quality measure is also presented to divide the unlabeled patterns into reliable and unreliable relabeled patterns. The reliable relabeled patterns are selected to be added into the labeled data for learning the labels of the unreliable relabeled data recurrently. Experimental results clearly demonstrate that the proposed method outperforms the state-of-the-art classification methods.
Keyword:
Semi-supervised classification
Multi-feature
Label membership
Sparse representation
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