Correntropy-Induced Tensor Learning for Multi-view Subspace Clustering

Using some specific optimization problems with specific regularizers, multi-view subspace clustering has achieved better performance over single-view subspace clustering. However, they simply assume the noise obeys the Gaussian distribution only, and thus the dataset with non-Gaussian noise or outliers may not be accurately clustered. To address this issue, this paper proposes a novel correntropy-induced tensor learning method for multi-view subspace clustering (CTMSC). Specifically, CTMSC adopts the correntropy-induced metric to substitute the traditional mean square error (MSE) to handle non-Gaussian noise or outliers. Furthermore, the proposed objective function is optimized using an alternating direction method of multipliers with the aid of half-quadratic technology in the form of multiplication. Extensive experimental results on various real-world datasets demonstrate the effectiveness of the proposed method by comparing several state-of-the-art multi-view subspace clustering methods.