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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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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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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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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A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization [PDF]
ISRN Applied Mathematics, 2013 As we all known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing and signal processing etc.
Lai, Shu-Zhen +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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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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