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Whole-brain modular dynamics at rest predict sensorimotor learning performance. [PDF]

Netw Neurosci
Standage DI, Gale DJ, Nashed JY, Flanagan JR, Gallivan JP.   +4 more
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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.
Yalan Qin, Guorui Feng, Yanli Ren, Xinpeng Zhang   +3 more
semanticscholar   +2 more sources

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
semanticscholar   +2 more sources

Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis

IEEE Transactions on Neural Networks and Learning Systems, 2021
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.
Xin Luo, Zhigang Liu, Long Jin, Yue Zhou, Mengchu Zhou   +4 more
semanticscholar   +3 more sources

Orthogonal Symmetric Nonnegative Matrix Tri-Factorization

2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)
Symmetric nonnegative matrix tri-factorization (trisymNMF) factorizes a symmetric input n-by-n matrix, $X$, using two matrices, a nonnegative $n$-by-$r$ matrix $W$ and a nonnegative symmetric $r$-by-$r$ matrix $S$, such that $X\approx WSW^{\mathrm{T ...
Alexandra Dache, A. Vandaele, Nicolas Gillis   +2 more
semanticscholar   +2 more sources

Pairwise Constraint Propagation-Induced Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems, 2018
As a variant of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) has shown to be effective for capturing the cluster structure embedded in the graph representation.
Wenhui Wu, Yuheng Jia, S. Kwong, Junhui Hou   +3 more
semanticscholar   +3 more sources

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.
Liangshao Hou, D. Chu, L. Liao
semanticscholar   +3 more sources

Symmetric nonnegative matrix factorization: A systematic review

Neurocomputing, 2023
Wensheng Chen, Kexin Xie, R. Liu, Binbin Pan   +3 more
semanticscholar   +2 more sources

A unified framework of community hiding using symmetric nonnegative matrix factorization

Information Sciences
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dong Liu, Ruoxue Jia, Xia Liu, Wensheng Zhang   +3 more
semanticscholar   +3 more sources

Robust Semi-Supervised Community Detection Based on Symmetric Nonnegative Matrix Factorization

2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC)
The symmetric nonnegative matrix factorization (SNMF) model, renowned for its interpretability and efficiency, has been extensively adopted for community detection in the domain of complex networks.
Wenyun Xie, Siyuan Peng, Zhijing Yang
semanticscholar   +2 more sources