Results 131 to 140 of about 13,867 (163)

Block-Diagonal Guided Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Knowledge and Data Engineering, 2021
Symmetric nonnegative matrix factorization (SNMF) is effective to cluster nonlinearly separable data, which uses the constructed graph to capture the structure of inherent clusters. Nevertheless, many SNMF-based clustering approaches implicitly enforce either the sparseness constraint or the smoothness constraint with the limited supervised information
Yalan Qin, Guorui Feng, Yanli Ren, Xinpeng Zhang   +3 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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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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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 ...
Melisew Tefera Belachew
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Symmetric nonnegative matrix factorization: A systematic review

Neurocomputing, 2023
Wen-Sheng Chen, Kexin Xie, Rui Liu, Binbin Pan   +3 more
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Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection

Information Fusion, 2023
Zhigang Liu, Xin Luo, Zidong Wang, Xiaohui Liu   +3 more
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Structured subspace learning-induced symmetric nonnegative matrix factorization

Signal Processing, 2021
Abstract Symmetric NMF (SNMF) is able to determine the inherent cluster structure with the constructed graph. However, the mapping between the empirically constructed similarity representation and the desired one may contain complex structural and hierarchical information, which is not easy to capture with single learning approaches. Then, we propose
Yalan Qin, Hanzhou Wu, Guorui Feng
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Symmetric Nonnegative Matrix Factorization With Beta-Divergences

IEEE Signal Processing Letters, 2012
Nonnegative matrix factorization/approximation (NMF) is a recently developed technology for dimensionality reduction and parts based data representation. The symmetric NMF (SNMF) decomposition is a special case of NMF, in which both factors are identical. This paper discusses SNMF decomposition with beta divergences.
Min Shi, Qingming Yi, Jun Lv
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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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