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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Vydané v:	Expert systems with applications Ročník 249; číslo B; s. 123645
Hlavní autori:	Saberi-Movahed, Farid, Biswas, Bitasta, Tiwari, Prayag, Lehmann, Jens, Vahdati, Sahar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.09.2024
Predmet:	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
On-line prístup:	Získať plný text
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