A flexible semi-supervised feature extraction method for image classification

Abstract

This paper proposes a novel discriminant semi-supervised feature extraction for generic classification and recognition tasks. The paper has two main contributions. First, we propose a flexible linear semisupervised feature extraction method that seeks a non-linear subspace that is close to a linear one. The proposed method is based on a criterion that simultaneously exploits the discrimination information provided by the labeled samples, maintains the graph-based smoothness associated with all samples, regularizes the complexity of the linear transform, and minimizes the discrepancy between the unknown linear regression and the unknown non-linear projection. Second, we provide extensive experiments on four benchmark databases in order to study the performance of the proposed method. These experiments demonstrate much improvement over the state-of-the-art algorithms that are either based on label propagation or semi-supervised graph-based embedding.