MMFE: Multitask Multiview Feature Embedding

In data mining and pattern recognition area, the learned objects are often represented by the multiple features from various of views. How to learn an efficient and effective feature embedding for the subsequent learning tasks? In this paper, we address this issue by providing a novel multi-task multiview feature embedding (MMFE) framework. The MMFE algorithm is based on the idea of low-rank approximation, which suggests that the observed multiview feature matrix is approximately represented by the low-dimensional feature embedding multiplied by a projection matrix. In order to fully consider the particular role of each view to the multiview feature embedding, we simultaneously suggest the multitask learning scheme and ensemble manifold regularization into the MMFE algorithm to seek the optimal projection. Since the objection function of MMFE is multi-variable and non-convex, we further provide an iterative optimization procedure to find the available solution. Two real world experiments show that the proposed method outperforms single-task-based as well as state-of-the-art multiview feature embedding methods for the classification problem.