A structure noise-aware tensor dictionary learning method for high-dimensional data clustering

. Furthermore, we use low-rank tensor modeling to characterize the inherent correlations of clean data and adopt tensor dictionary learning to adaptively and accurately describe the structure noise instead of using the predefined distribution. We design the proximal alternating minimization algorithm to solve the proposed model with the theoretical convergence guarantee. Experimental results on both simulated and real datasets show that the proposed method outperforms the compared methods for high-dimensional data clustering.