Robust CP Tensor Factorization with Skew Noise

The low-rank tensor factorization (LRTF) technique has received increasing popularity in data science, especially in computer vision applications. Many robust LRTF models have been presented recently. However, none of them take the skewness of data into account. This letter proposes a novel LRTF model for skew data analysis by modeling noise as a Mixture of Asymmetric Laplacians (MoAL). The numerical experiments show that the new model MoAL-LRTF outperforms several state-of-the-art counterparts. The codes for all the experiments are available at https://xsxjtu.github.io/Projects/MoAL/main.html.