Robust self supervised symmetric nonnegative matrix factorization to the graph clustering [PDF]
Scientific ReportsGraph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities.
Yi Ru, Michael Gruninger, YangLiu Dou
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MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis [PDF]
BMC Bioinformatics, 2020 Background With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data).
Yuanyuan Ma, Junmin Zhao, Yingjun Ma
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Self-Supervised Symmetric Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2021 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 ...
Yuheng Jia +4 more
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SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association. [PDF]
RNA Biol, 2020 Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of ...
Zhao Y, Chen X, Yin J, Qu J.
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Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix [PDF]
Algorithms, 2019 The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a ...
Paola Favati +3 more
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A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 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.
Xiao Li, Zhihui Zhu, Qiuwei Li, Kai Liu
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An improved multi-view spectral clustering based on tissue-like P systems [PDF]
Scientific Reports, 2022 Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view.
Huijian Chen, Xiyu Liu
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Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization. [PDF]
Comput Soc Netw, 2017 Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities.We propose a divide-and-conquer strategy to discover hierarchical community ...
Du R, Kuang D, Drake B, Park H.
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Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and ApplicationsSymmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose.
Koby Hayashi +3 more
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Off-diagonal symmetric nonnegative matrix factorization [PDF]
Numerical Algorithms, 2020 Symmetric nonnegative matrix factorization (symNMF) is a variant of nonnegative matrix factorization (NMF) that allows handling symmetric input matrices and has been shown to be particularly well suited for clustering tasks.
François Moutier +2 more
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