New semi-supervised classification using a multi-modal feature joint L21-norm based sparse representation

Yan Cui, Jielin Jiang, Zhihui Lai, Zuojin Hu, Yuquan Jiang, Wai Keung Wong

Abstract: Highlights •We develop a new semi-supervised classification algorithm based on a multi-modal feature joint L21<math><msub is="true"><mrow is="true"><mi is="true">L</mi></mrow><mrow is="true"><mn is="true">21</mn></mrow></msub></math>-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.