AUTHOR=Linehan Kathryn , Balan Radu 

TITLE=CUR matrix approximation through convex optimization for feature selection

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1632218

DOI=10.3389/fams.2025.1632218

ISSN=2297-4687

ABSTRACT=The singular value decomposition (SVD) is commonly used in applications that require a low-rank matrix approximation. However, the singular vectors cannot be interpreted in terms of the original data. For applications requiring this type of interpretation, e.g., selection of important data matrix columns or rows, the approximate CUR matrix factorization can be used. Work on the CUR matrix approximation has generally focused on algorithm development, theoretical guarantees, and applications. In this study, we present a novel deterministic CUR formulation and algorithm with theoretical convergence guarantees. The algorithm utilizes convex optimization, finds important columns and rows separately, and allows the user to control the number of important columns and rows selected from the original data matrix. We present numerical results and demonstrate the effectiveness of our CUR algorithm as a feature selection method on gene expression data. These results are compared to those using the SVD and other CUR algorithms as the feature selection method. Finally, we present a novel application of CUR as a feature selection method to determine discriminant proteins when clustering protein expression data in a self-organizing map (SOM), and compare the performance of multiple CUR algorithms in this application.