Learning a Discriminative Isotropic Space from Labeled and Unlabeled Data

Euclidean distance measure is computationally simple and commonly used in the task of classification. However, it does not capitalize on any discriminant information from training samples, which is crucial to classification performance. In this paper, we learn a Discriminative Isotropic Space from both labeled and unlabeled data to improve classification accuracy as well as generalization ability. Our regularized objective function implicitly minimizes mutual information between input space and transformed space. There is no local optimum problem in our algorithm and its closed form solution can be easily obtained. Experiments on real data sets demonstrate the efficacy of our method.