Adaptive multi-view subspace clustering for high-dimensional data

Abstract

With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims to determine the distribution of views by extra empirical parameters and search the optimal projection matrix by eigenvalue decomposition, which is impractical for real-world applications. In this paper, we propose a new adaptive multi-view subspace clustering method to integrate heterogenous data in the low-dimensional feature space. Concretely, we extend K-means clustering with feature learning to handle high-dimensional data. Besides, for multi-view data, we evaluate the weights of distinct views according to their compactness of the cluster structure in the low-dimensional subspace. We apply the proposed method to four benchmark datasets and compare it with several widely used clustering algorithms. Experimental results demonstrate the effectiveness of the proposed method.