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...

Full description

Bibliographic Details

Published in:	Expert systems with applications Vol. 249; no. B; p. 123645
Main Authors:	Saberi-Movahed, Farid, Biswas, Bitasta, Tiwari, Prayag, Lehmann, Jens, Vahdati, Sahar
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2024
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!