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 L21−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.
- Label membership
- Semi-supervised classification
- Sparse representation
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering