Thresholding Approach for Low-Rank Correlation Matrix Based on MM Algorithm.

Background: Low-rank approximation is used to interpret the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in an estimation that is far from zero, even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation.

Methods: To overcome this, we propose a novel approach to estimate a sparse low-rank correlation matrix based on threshold values.