Kernelized Multiview Subspace Analysis By Self-Weighted Learning

With the popularity of multimedia technology, information is always represented from multiple views. Even though multiview data can reflect the same sample from different perspectives, multiple views are consistent to some extent because they are representations of the same sample. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlook the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview-based methods have been proposed for dimension reduction. However, there is still no research that leverages the existing work into a unified framework. In this paper, we propose a general framework for multiview data dimension reduction, named kernelized multiview subspace analysis (KMSA) to handle multiview feature representation in the kernel space, providing a feasible channel for multiview data with different dimensions. Compared with the graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively. A co-regularized term is proposed to promote the mutual learning from multiviews. KMSA combines self-weighted learning with the co-regularized term to learn the appropriate weights for all views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method.