Discriminative semi-supervised feature selection via manifold regularization

We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed method selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recen...

• Type: Journal
• Year: 2010
• Where: TNN
• Authors: Zenglin Xu, Irwin King, Michael R. Lyu, Rong Jin
• Keywords: Feature Selection, Feature Selection Method, Management, Semi-supervised Feature Selection, TNN 2010