Results 151 to 160 of about 21,847 (188)

Temporal community detection based on symmetric nonnegative matrix factorization

International Journal of Modern Physics B, 2017
To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split ...
Pengfei Jiao, Haodong Lyu, Xiaoming Li, Wei Yu, Wenjun Wang   +4 more
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A Block Inertial Bregman Proximal Algorithm for Nonsmooth Nonconvex Problems with Application to Symmetric Nonnegative Matrix Tri-Factorization

Journal of Optimization Theory and Applications, 2020
We propose BIBPA, a block inertial Bregman proximal algorithm for minimizing the sum of a block relatively smooth function (that is, relatively smooth concerning each block) and block separable nonsmooth nonconvex functions.
Masoud Ahookhosh, L. Hien, Nicolas Gillis, Panagiotis Patrinos   +3 more
semanticscholar   +1 more source

Sparse symmetric nonnegative matrix factorization applied to face recognition

2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017
The task of Sparse Symmetric Nonnegative Matrix Factorization(SSNMF) is formulated as optimization problem and solved numerically with the method of projected gradients descent. The adjustable sparsity level allows to emphasize the most significant object features.
Hennadii Dobrovolskyi, Nataliya Keberle, Yehor Ternovyy   +2 more
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Block Iteratively Reweighted Algorithms for Robust Symmetric Nonnegative Matrix Factorization

IEEE Signal Processing Letters, 2018
This letter is concerned with the symmetric nonnegative matrix factorization in the presence of heavy-tailed outliers. We address this problem under a formulation involving some robust loss functions, instead of the standard squared-error loss. To handle the original computationally intractable problem, we present an efficient block iteratively ...
Zhen-Qing He, Xiaojun Yuan
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Efficient algorithm for sparse symmetric nonnegative matrix factorization

Pattern Recognition Letters, 2019
Abstract Symmetric Nonnegative Matrix Factorization (symNMF) is a special case of the standard Nonnegative Matrix Factorization (NMF) method which is the most popular linear dimensionality reduction technique for analyzing nonnegative data. Examples of symmetric matrices that arise in real-life applications include covariance matrices in finance ...
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Multi-view clustering via graph regularized symmetric nonnegative matrix factorization

2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2016
Multi-view clustering has become a hot topic since the past decade and nonnegative matrix factorization (NMF) based multi-view clustering algorithms have shown their superiorities. Nevertheless, two drawbacks prevent NMF based multi-view algorithms from being a better algorithm: (1) The solution of NMF based multi-view algorithms is not unique.
Xianchao Zhang, null Zhongxiu Wang, Linlin Zong, Hong Yu   +3 more
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Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach

IEEE Transactions on Industrial Informatics, 2020
Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns.
Yan Song, Ming Li, Xin Luo, Guisong Yang, Chongjing Wang   +4 more
semanticscholar   +1 more source

Integrating Symmetric Nonnegative Matrix Factorization and Normalized Cut Spectral Clustering

2010 IEEE International Conference on Data Mining Workshops, 2010
In this paper, we integrate symmetric NMF and normalized cut into a single clustering framework and derive the computational algorithm. Another contribution is to provide a new matrix inequality which is useful for the analysis of 4-th order matrix polynomials.
Zhichen Xia, Chris Ding
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Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and
Tianxiang Gao, Sigurdur Olofsson, Songtao Lu   +2 more
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A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

2018
This paper presents a collaborative neurodynamic approach to symmetric nonnegative matrix factorization (SNMF). First, a formulated nonconvex optimization problem of SNMF is described. To solve this problem, a neurodynamic model based on an augmented Lagrangian function is proposed and proven to be convergent to a strict local optimal solution under ...
Hangjun Che, Jun Wang
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