Non-negative tensor decomposition has achieved significant success in machine learning
due to its superiority in extracting the non-negative parts-based features and physically …

Unlike traditional tensor decompositions which model low-rankness in the original domain,
the recently proposed tensor* L-Singular Value Decomposition (* L-SVD) casts a new light …

Tensor robust principal component analysis (TRPCA) is a useful approach for obtaining low-
rank data corrupted by noise or outliers. However, existing TRPCA methods face certain …

This paper conducts a rigorous analysis for the problem of robust tensor completion, which
aims at recovering an unknown three-way tensor from incomplete observations corrupted by …

Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating
low-rankness in the spectral domain over other tensor decompositions (like Tucker …

Recently, methods for modeling images in frequency domain have attracted widespread
attention. Frequency methods transform image into spectrum as model input by defining a …

Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and
sparse components from multi-dimensional data, making it an essential technique in the …

Compared to widely examined topics in the related literature, such as financial
crises/difficulties in accurate prediction, studies on corporate performance forecasting are …