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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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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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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Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding. [PDF]
PLoS ONEText embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose.
Mingming Li +3 more
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Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering [PDF]
IEEE Access, 2019 As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity.
Wei Yan +3 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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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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IEEE Access, 2020 Community detection is a critical issue in the field of complex networks. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks.
Hong Lu +3 more
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