Robust Adaptive Label Propagation by Double Matrix Decomposition

Abstract

In this paper, we investigate the robust transductive label prediction problem. Technically, a Robust Adaptive Label Propagation framework by Double Matrix Decomposition, called ALP-MD, is proposed for the semi-supervised data classification. Compared with existing transductive label propagation models, our ALP-MD improves the classification power by performing label prediction in the clean data space and clean label space at the same time. More specifically, our ALP-MD clearly integrates the idea of double matrix decomposition into the process of label prediction for the noise removal. Since the predicted soft labels usually contains noise and mixed signs, our ALP-MD explicitly decomposes the predicted soft label matrix into a clean soft label matrix and a noise term and then estimates the hard label based on the clean soft label matrix for more accurate classification. In addition, ALP-MD also involves a regularization term to model the noise in data, integrates the adaptive weights learning into the process of robust label prediction and moreover performs the weights learning in the clean data space. Thus, our ALP-MD can explicitly ensure the learned weights to be informative as much as possible and to be joint optimal for both representation and classification, and potentially enhance the label prediction ability. Extensive comparisons demonstrated its effectiveness.