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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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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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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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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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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An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation [PDF]
Symmetry, 2020 Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering. In this paper, we propose an accelerated variant of the multiplicative update (MU) algorithm of He et al ...
Zhaoshui He, Ji Tan, Beihai Tan
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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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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. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation.
Haesun Park +2 more
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Efficient and Non-Convex Coordinate Descent for Symmetric Nonnegative Matrix Factorization
IEEE Transactions on Signal Processing, 2016 Given a symmetric nonnegative matrix $A$ , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$ , usually with much fewer columns than $A$ , such that $A \approx HH^T$ . SymNMF can be used for data analysis and in particular for various clustering tasks.
Nicolas Gillis +2 more
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