Inductive and flexible feature extraction for semi-supervised pattern categorization

摘要

This paper proposes a novel discriminant semi-supervised feature extraction method for generic classification and recognition tasks. This method, called inductive flexible semi-supervised feature extraction, is a graph-based embedding method that seeks a linear subspace close to a non-linear one. It 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. We extend the proposed method to the case of non-linear feature extraction through the use of kernel trick. This latter allows to obtain a nonlinear regression function with an output subspace closer to the learned manifold than that of the linear one. Extensive experiments are conducted on ten benchmark databases in order to study the performance of the proposed methods. Obtained results demonstrate a significant improvement over state-of-the-art algorithms that are based on label propagation or semi-supervised graph-based embedding. (C) 2016 Elsevier Ltd. All rights reserved.