Deep Nonnegative Matrix Factorization with Joint Global and Local Structure Preservation

Deep Non-Negative Matrix Factorization (DNMF) methods provide an efficient low-dimensional representation of given data through their layered architecture. A limitation of such methods is that they cannot effectively preserve the local and global geometric structures of the data in each layer. Conse...

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Veröffentlicht in:	Expert systems with applications Jg. 249; H. B; S. 123645
Hauptverfasser:	Saberi-Movahed, Farid, Biswas, Bitasta, Tiwari, Prayag, Lehmann, Jens, Vahdati, Sahar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2024
Schlagworte:	Deep Non-negative Matrix Factorization Global structure Local structure Non-negative Matrix Factorization Sparsity Non-negative Matrix Factorization Global structure Local structure Deep Non-negative Matrix Factorization Sparsity
ISSN:	0957-4174, 1873-6793, 1873-6793
Online-Zugang:	Volltext
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