Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

To improve the sparseness of the base matrix in incremental non-negative matrix factorization, we in this paper present a new method, orthogonal incremental non-negative matrix factorization algorithm (OINMF), which combines the orthogonality constraint with incremental learning. OINMF adopts batch...

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Veröffentlicht in:	Computational & applied mathematics Jg. 39; H. 2
Hauptverfasser:	Ge, Shaodi, Luo, Liuhong, Li, Hongjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.05.2020 Springer Nature B.V
Schlagworte:	Algorithms Applications of Mathematics Applied physics Computational mathematics Computational Mathematics and Numerical Analysis Factorization Image classification Machine learning Manifolds (mathematics) Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Mathematics Mathematics and Statistics Orthogonality 68U10 Incremental learning Orthogonality constraints Non-negative factorization 68W25 Sparseness Image classification
ISSN:	2238-3603, 1807-0302
Online-Zugang:	Volltext
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