Original paper
Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix
Volume: 12, Issue: 6, Pages: 1378 - 1389
Published: Dec 1, 2018
Abstract
Robust principal component analysis (RPCA) has been widely used for many data analysis problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to extract the low rank and sparse components of multidimensional data, which is a generation of RPCA. The current RTPCA methods are directly based on tensor singular value decomposition (t-SVD), which is a new tensor decomposition method similar to singular value decomposition...
Paper Details
Title
Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix
Published Date
Dec 1, 2018
Volume
12
Issue
6
Pages
1378 - 1389