Nonnegative matrix factorizationSupervised informationCorrentropyOutliersImage clusteringNonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in
Robust nonnegative matrix factorization via ℓ1 norm regularization,” ArXiv preprint arXiv:1204.2311 - Shen, Si, et al. - 2012 () Citation Context ...lem. Robust NMF is a nonnegative variant of robust PCA [8] which has appeared in different forms in the literature. In [9], the ...
Nonnegative matrix factorizationCo-clustering is to group features and samples simultaneously and has received increasing attention in data mining and machine learning, particularly in text document categorization and gene expression. In this paper, two effective co-clustering algorithms are proposed to ...
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Robust nonnegative matrix factorization using l21-norm. In: Proceedings of the 20th ACM international conference on Information and knowledge management. New York: ACM (Association for Computing Machinery); 2011. pp. 673–682. 58. Dang Q, Liang Y, Ouyang D, Miao R, Ling C, Liu X, ...
Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification task. However, the existing methods do not consider robustness, adaptive graph learning and discrimination information at the same time. To solve this problem, a new nonnegative matrix ...
Similarly to previous works36,40, we found that four muscle synergies, extracted using the non-negative matrix factorization algorithm (NMF)41 could satisfactorily reconstruct the EMG activity of all subjects during baseline, adaptation and washout (average Variance Accounted For (VAF) across subjects ...
40. Yu N, Liu J-X, Gao Y-L, Zheng C-H, Wang J, Wu M-J: Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM); 2017. IEEE, pp. 1752–1756. 4...
Peng S, Ser W, Chen B, Lin Z (2021) Robust semi-supervised nonnegative matrix factorization for image clustering. Pattern Recognition 111:107683 Google Scholar Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD interna...
Nonnegative Matrix Factorization (NMF) has received much attention in data clustering due to its intuitive parts-based interpretation [30], [31]. Previous studies have shown that NMF is essentially equal to k-means with a relaxed condition [30]. Here we start with an introduction to NMF [32...