Simultaneous label inference and discriminant projection estimation through adaptive self-taught graphs

Abstract

Processing structured data has become an interesting topic in recent years. The development of graph-based semi-supervised learning models has attracted much attention from machine learning researchers. In this paper, we present a novel approach for graph-based semi-supervised learning. We provide an effective method for simultaneous label recovery and linear transformation estimation. The targeted linear transformation is to obtain a discriminant subspace. The most important factor in this work to improve the semi-supervised learning is to exploit the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the labels increases the monitoring information in an indirect way through an introduced label-graph, avoiding the use of confidence-based hard decisions as used in self-supervised methods. It also enforces label smoothing and projected data smoothing through the use of hybrid graphs. For each smoothing type, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data information and the similarity of the label information. The proposed method leads to an improved discriminant linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. This work also shows superior performance compared to semi-supervised methods that use simultaneous embedding and inference of labels.