Results 151 to 160 of about 13,849 (192)

Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

IEEE Transactions on Neural Networks, 2011
Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition.
Zhaoshui, He, Shengli, Xie, Rafal, Zdunek, Guoxu, Zhou, Andrzej, Cichocki   +4 more
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A Progressive Hierarchical Alternating Least Squares Method for Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
In this article, we study the symmetric nonnegative matrix factorization (SNMF) which is a powerful tool in data mining for dimension reduction and clustering. The main contributions of the present work include: (i) a new descent direction for the rank-one SNMF is derived and a strategy for choosing the step size along this descent direction is ...
Liangshao Hou, Delin Chu, Li-Zhi Liao
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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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Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis

IEEE Transactions on Neural Networks and Learning Systems, 2022
Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative update (NMU) scheme is frequently adopted to address it. Current research mainly focuses on integrating additional information into it without considering the effects of a ...
Xin Luo, Zhigang Liu, Long Jin, Yue Zhou, Mengchu Zhou   +4 more
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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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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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