Fast label prediction based on shrunk anchor graph for semi-supervised incomplete multiview classification

Existing anchor graph-based semi-supervised classification methods can not adopt partial available labels of data to produce discriminative anchor graph, which is even challenging for incomplete multi-view data. Addressing above issues, a fast label prediction based on shrunk anchor graph (FLP-SAG) is designed for semi-supervised incomplete multi-view classification, which is capable of learning discriminative anchor graph iteratively. Firstly, in each view a similarity-based anchor graph is constructed and expanded to the size of complete data to align the multiple views. Then these pre-constructed anchor graphs are fused to get a common anchor graph, which is ready to be shrunk based on the predicted labels with high confidence scores in each iteration. To speed up the classification, an efficient two-step label prediction strategy is developed without the calculation of dense matrix inverse. Experimental results on four real world datasets comparing with several recently proposed methods demonstrate the superiority of the proposed method.