CUR matrix approximation through convex optimization for feature selection

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 co...

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Bibliographic Details

Published in:	Frontiers in Applied Mathematics and Statistics
Main Authors:	Kathryn Linehan, Radu Balan
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-08-01
Subjects:	CUR matrix approximation convex optimization low-rank matrix approximation feature selection interpretation
Online Access:	https://www.frontiersin.org/articles/10.3389/fams.2025.1632218/full