Cited By
View all- Lang CWang ZHe KSun S(2022)POI recommendation based on a multiple bipartite graph network modelThe Journal of Supercomputing10.1007/s11227-021-04279-178:7(9782-9816)Online publication date: 1-May-2022
Non-negative matrix factorization via adaptive sparse graph regularization
Pages 12507 - 12524
Abstract
Non-negative matrix factorization (NMF), as an efficient and intuitive dimension reduction algorithm, has been successfully applied to clustering tasks. However, there are still two dominating limitations. First, the original NMF only pays attention to the global data structure, ignoring the intrinsic geometry of the original higher-dimensional data. Second, the traditional pairwise distance-based graph construction is sensitive to noise and outliers, and the nearest neighbor graph obtained is not optimal. As a result, the clustering performance will be reduced. To solve the aforementioned problems and increase the cluster accuracy, a non-negative matrix factorization via adaptive sparse graph (NMF_ASGR) is proposed in this paper. More precisely, this paper assembles the sparse representation and manifold learning into a framework to get the l1 sparse robust graph. The l1 sparse robust graph not only can adaptively discover the potential manifold structure of the data, but also has strong robustness to noise and outliers, which makes the structure of the graph can be learned automatically in the process of matrix decomposition. Moreover, an adaptive sparse graph is learned to batter regularize the NMF. Finally, the effectiveness and superiority of the proposed algorithm are illustrated by lots of image clustering experiments.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.
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Kluwer Academic Publishers
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Publication History
Published: 01 March 2021
Accepted: 09 December 2020
Revision received: 12 October 2020
Received: 21 March 2020
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View all- Lang CWang ZHe KSun S(2022)POI recommendation based on a multiple bipartite graph network modelThe Journal of Supercomputing10.1007/s11227-021-04279-178:7(9782-9816)Online publication date: 1-May-2022