Semi-Supervised Classification Based on Transformed Learning

In recent years graph-based semi-supervised classification is one of the research hot topics in machine learning and pattern recognition. In general, this algorithm discovers the hidden information by constructing a graph and classifies the labels for unlabeled samples based on the structural information of the graph. Therefore, the performance of semi-supervised classification heavily depends on the quality of the graph, especially the graph construction algorithm and the quality of data. In order to solve the above problems, we propose to perform a semi-supervised classification based on transformed learning (TLSSC) in this paper. Unlike most existing semi-supervised classification algorithms that learn the graph using raw features, our algorithm seeks a representation (transformed coefficients) and performs graph learning and label propagation based on the learned representation. In particular, a unified framework that integrates representation learning, graph construction, and label propagation is proposed, so that it is alternately updated and mutually improved and can avoid the sub-optimal solution caused by the low-quality graph. Specially, the raw features are mapped into transformed representation by transformed learning, then learn a high-quality graph by self-expression and achieve classification performance by label propagation. Extensive experiments on face and subject data sets show that our proposed algorithm outperforms other state-of-the-art algorithms in most cases.