Supervised sparse coding with local geometrical constraints

Abstract

Sparse coding algorithms with geometrical constraints have received much attention recently. However, these methods are unsupervised and might lead to less discriminative representations. In this paper, we propose a supervised locality-constrained sparse coding method for classification. Two graphs are constructed, a labeled graph and an unlabeled graph. Sparse codes with a labeled geometrical constraint will be more discriminative, however we cannot embed test samples with unknown label into a labeled graph. By coupling the two graphs, we aim to make the difference between sparse codes with labeled and unlabeled geometrical constraints as small as possible. As a result, sparse codes of test data can be obtained with the unlabeled geometrical constraint and the discrimination of the labeled geometrical constraint is maintained. Experiments on some benchmark datasets demonstrate the effectiveness of the proposed method.