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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Vydáno v:Computational & applied mathematics Ročník 39; číslo 2
Hlavní autoři: Ge, Shaodi, Luo, Liuhong, Li, Hongjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.05.2020
Springer Nature B.V
Témata:
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
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Shrnutí: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 update in the process of incremental learning, and its iterative formulae are obtained using the gradient on the Stiefel manifold. The experiments on image classification show that the proposed method achieves much better sparseness and orthogonality, while retaining time efficiency of incremental learning.
Bibliografie:ObjectType-Article-1
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ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-020-1091-2