Tensor Robust Principal Component Analysis with Low-Rank Weight Constraints for Sample Clustering

Abstract

With the rapid development of the next-generation sequencing technology, a large amount of genomics information has been obtained. The scale of biological sequencing data is particularly large and complex. The tensor robust principal component analysis (TRPCA) method can effectively preserve the spatial structure of tensor data, so it has received extensive attention. However, the low-rank tensor obtained by TRPCA may be damaged to a certain extent. To solve this problem, this paper proposes a model for weighting low-rank data based on the method of TRPCA. This model has an additional constraint penalty term that can repair corrupted low-rank data and the effective information in it can be fully utilized. In addition, the norm is used to constrain the sparse tensor to make the sparse effect better. In the experimental part, TRPCA model clusters samples by low-rank tensor. The experimental results on cancer omics data show that our method is superior to other methods.