Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 10, Issue 6, Pages 1259-1268Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-018-0808-7
Keywords
Nonnegative matrix factorization; Graph regularized nonnegative matrix factorization; Incremental nonnegative matrix factorization; Incremental graph regularized nonnegative matrix factorization; Correlation
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Funding
- National Natural Science Fund of China [71471060]
- Fundamental Research Funds for the Central Universities Support Program [2015xs71]
- China Scholarship Council
- National Energy Research Scientific Computing Center (NERSC)
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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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