Adaptive Latent Representation for Multi-view Subspace Learning

In recent years, datasets represented in multi-view contain more information because different views describe different aspects. In fact, the structure of the dataset is embedded in a union of certain low-dimensional subspaces. Therefore, multiview subspace learning is a powerful technology to find the underlying structure and cluster data points correctly. Actually, the information contained in different views is different. Furthermore, the original data may be noisy. However, most existing multi-view subspace clustering methods treat each view equally and learn the self-expressiveness coefficient matrix of each view on the original data, which would decrease clustering performance. To solve the above problems, we propose a new method which learns a latent representation in an adaptive way. Meanwhile, the local geometrical structure is maintained on the latent representation by graph regularization. At the same time, a basic subspace clustering method is performed on the latent representation to get self-expressiveness coefficient matrix. We formulate the above problems into a unified optimization framework. Experimental results on several real-world datasets show the effectiveness of the proposed method.