matrixfactorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegativematrixfactorization and its incremental variants have ...
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matrixfactorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegativematrixfactorization 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 incrementalnonnegativematrixfactorization 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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