Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network

Orthogonal Nonnegative Matrix Factorization (ONMF) offers an important analytical vehicle for addressing many problems. Encouraged by record-breaking successes attained by neural computing models in solving an assortment of data analytics tasks, a rich collection of neural computing models has been...

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Veröffentlicht in:	Knowledge-based systems Jg. 227; S. 107236
Hauptverfasser:	Yang, Mingming, Xu, Songhua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 05.09.2021 Elsevier Science Ltd
Schlagworte:	Auxiliary function Coders Complexity Computation Convergence Deep autoencoder network Deep structure Experiments Factorization Grammatical aspect Iterative methods Matrices Multiplication update rule Networks Novels Orthogonal Nonnegative Matrix Factorization Retraining Uniqueness Deep autoencoder network Multiplication update rule Orthogonal Nonnegative Matrix Factorization Auxiliary function
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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