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...

書誌詳細
出版年:	Frontiers in Applied Mathematics and Statistics
主要な著者:	Kathryn Linehan, Radu Balan
フォーマット:	論文
言語:	英語
出版事項:	Frontiers Media S.A. 2025-08-01
主題:	CUR matrix approximation convex optimization low-rank matrix approximation feature selection interpretation
オンライン･アクセス:	https://www.frontiersin.org/articles/10.3389/fams.2025.1632218/full

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