New semi-supervised classification using a multi-modal feature joint L21-norm based sparse representation
作者：
Highlights：
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• We develop a new semi-supervised classification algorithm based on a multi-modal feature joint L21-norm sparse representation.
• In the proposed optimization, the labeled patterns are sparsely represented by the abundant of unlabeled patterns, 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 labeled data set. The reliable labeled patterns are selected to add into the labeled data to learn the labels of the unreliable labeled data recurrently.
摘要
•We develop a new semi-supervised classification algorithm based on a multi-modal feature joint L21-norm sparse representation.•In the proposed optimization, the labeled patterns are sparsely represented by the abundant of unlabeled patterns, 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 labeled data set. The reliable labeled patterns are selected to add into the labeled data to learn the labels of the unreliable labeled data recurrently.
论文关键词：Semi-supervised classification,Multi-feature,Label membership,Sparse representation
论文评审过程：Received 14 August 2017, Revised 19 February 2018, Accepted 12 March 2018, Available online 23 March 2018, Version of Record 6 April 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.03.005
