Results 11 to 20 of about 13,849 (192)
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 ...
François Moutier +2 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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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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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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Some upper and lower bounds on PSD-rank [PDF]
, 2014 Positive semidefinite rank (PSD-rank) is a relatively new quantity with applications to combinatorial optimization and communication complexity. We first study several basic properties of PSD-rank, and then develop new techniques for showing lower bounds
de Wolf, Ronald, Lee, Troy, Wei, Zhaohui
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Adaptive computation of the Symmetric Nonnegative Matrix Factorization (SymNMF)
SeMA Journal, 2020 Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is ...
P. Favati +3 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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Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization
SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020 We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy.
Srinivas Eswar +5 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 ...
Wang, Peitao +7 more
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Inexact Block Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Signal Processing, 2017 Symmetric nonnegative matrix factorization (SNMF) is equivalent to computing a symmetric nonnegative low rank approximation of a data similarity matrix. It inherits the good data interpretability of the well-known nonnegative matrix factorization technique and have better ability of clustering nonlinearly separable data.
Shi, Qingjiang +4 more
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