Multi-view Uncorrelated Linear Discriminant Analysis with Applications to Handwritten Digit Recognition

Learning from multiple feature sets, which is also called multi-view learning, is more robust than single view learning in many real applications. Canonical correlation analysis (CCA) is a popular technique to utilize information from multiple views. However, as an unsupervised method, it does not exploit the label information. In this paper, we propose an algorithm which combines uncorrelated linear discriminant analysis (ULDA) with CCA, named multi-view uncorrelated linear discriminant analysis (MULDA). Due to the successful application of ULDA, which seeks optimal discriminant features with minimum redundancy in the single view situation, it could be expected that the recognition performance would be enhanced. Experiments on handwritten digit data verify this expectation with results outperform other related methods.