A Semi-Supervised Framework for Feature Mapping and Multiclass Classification
Bo Chen, Wai Pang Lam, Ivor Wai-Hung Tsang, Tak-Lam Wong · 2009
We propose a semi-supervised framework incorporating feature mapping with multiclass classification. By learning multiple classification tasks simultaneously, this framework can learn the latent feature space effectively for both labeled and unlabeled data. The knowledge in the transformed space can be transferred not only between the labeled and unlabeled data, but also across multiple classes, so as to improve the classification performance given a small amount of labeled data. We show that this problem is equivalent to a sequential convex optimization problem by applying constraint concave-convex procedure (CCCP). Efficient algorithm with theoretical guarantee is proposed and computational issue is investigated. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed framework.