Results 11 to 20 of about 546 (144)
WSNMF: Weighted Symmetric Nonnegative Matrix Factorization for attributed graph clustering
NeurocomputingIn recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph clustering. Nevertheless, when applied to attributed graph clustering, it confronts notable challenges.
Razieh Sheikhpour +2 more
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Block Sparse Symmetric Nonnegative Matrix Factorization Based on Constrained Graph Regularization [PDF]
Jisuanji kexue, 2023 The existing algorithms based on symmetric nonnegative matrix factorization(SymNMF) are mostly rely on initial data to construct affinity matrices,and neglect the limited pairwise constraints,so these methods are unable to effectively distinguish similar
LIU Wei, DENG Xiuqin, LIU Dongdong, LIU Yulan
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Off-diagonal symmetric nonnegative matrix factorization [PDF]
Numerical Algorithms, 2021 Symmetric nonnegative matrix factorization (symNMF) is a variant of nonnegative matrix factorization (NMF) that allows to handle symmetric input matrices and has been shown to be particularly well suited for clustering tasks. In this paper, we present a new model, dubbed off-diagonal symNMF (ODsymNMF), that does not take into account the diagonal ...
Moutier, François +2 more
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Self-Supervised Symmetric Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2022 Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering ...
Yuheng Jia +4 more
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Frontiers in Molecular Biosciences, 2021 A network is an efficient tool to organize complicated data. The Laplacian graph has attracted more and more attention for its good properties and has been applied to many tasks including clustering, feature selection, and so on.
Junmin Zhao, Yuanyuan Ma, Lifang Liu
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A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization
IEEE Transactions on Knowledge and Data Engineering, 2021 The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that ...
Xiao Li 0009 +3 more
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Orthogonal Symmetric Nonnegative Matrix Tri-Factorization
2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)Nicolas Gillis, Arnaud Vandaele
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Symmetric Nonnegative Matrix Factorization Based on Box-Constrained Half-Quadratic Optimization
IEEE Access, 2020 Nonnegative Matrix Factorization (NMF) based on half-quadratic (HQ) functions was proven effective and robust when dealing with data contaminated by continuous occlusion according to the half-quadratic optimization theory.
Bo-Wei Chen
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Robust Community Detection in Graphs
IEEE Access, 2021 Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint ...
Esraa M. Al-Sharoa +2 more
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CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization [PDF]
Neurocomputing, 2018 Abstract Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis.
Li-Ping Tian 0001 +4 more
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