Thresholding Approach for Low-Rank Correlation Matrix based on MM algorithm

Abstract

BackgroundLow-rank approximation is used for interpreting the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in estimation that is far from zero even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. MethodsTo overcome this, we propose a novel approach to estimate a sparse low-rank correlation matrix based on threshold values. We introduce a new cross-validation function to tune the corresponding threshold values. To calculate the value of a function, the MM algorithm is used to estimate the sparse low-rank correlation matrix, and a grid search was performed to select the threshold values. ResultsThrough numerical simulation, we found that the false positive rate (FPR) and average relative error of the proposed method were superior to those of the tandem approach. For the application of microarray gene expression, the FPRs of the proposed approach with d = 2, 3, and 5 were 0.128, 0.139, and 0.197, respectively, whereas the FPR of the tandem approach was 0.285. ConclusionsWe propose a novel approach to estimate sparse low-rank correlation matrices. The advantage of the proposed method is that it provides results that are interpretable through the use of a heatmap, thereby avoiding result misinterpretations. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.