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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2019

15-03-2018 | Original Article

Incremental nonnegative matrix factorization based on correlation and graph regularization for matrix completion

Authors: Xiaoxia Zhang, Degang Chen, Kesheng Wu

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2019

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Abstract

Matrix factorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegative matrix factorization and its incremental variants have attracted much attention. The existing incremental algorithms are established based on the assumption of samples are independent and only update the new latent variable of weighting coefficient matrix when the new sample comes, which may lead to inferior solutions. To address this issue, we investigate a novel incremental nonnegative matrix factorization algorithm based on correlation and graph regularizer (ICGNMF). The correlation is mainly used for finding out those correlated rows to be updated, that is, we assume that samples are dependent on each other. We derive the updating rules for ICGNMF by considering the correlation. We also present tests on widely used image datasets, and show ICGNMF reduces the error by comparing other methods.
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Footnotes
2
 
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Metadata
Title
Incremental nonnegative matrix factorization based on correlation and graph regularization for matrix completion
Authors
Xiaoxia Zhang
Degang Chen
Kesheng Wu
Publication date
15-03-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0808-7

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