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Efficient and Non-Convex Coordinate Descent for Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Signal Processing, 2016
Given a symmetric nonnegative matrix $A$ , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$ , usually with much fewer columns than $A$ , such that $A \approx HH^T$ . SymNMF can be used for data analysis and in particular for various clustering tasks.
Nicolas Gillis, Arnaud Vandaele, Lei Qi
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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.
Songtao Lu, Haoran Sun, Qingjiang Shi
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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
openaire   +1 more source

Orthogonal Symmetric Nonnegative Matrix Tri-Factorization

2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)
Nicolas Gillis, Arnaud Vandaele
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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
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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. In contrast to the existing SNMF-based clustering methods that empirically construct the similarity matrix and rigidly introduce the supervisory information to the assignment ...
Sam Kwong, Wenhui Wu, Yuheng Jia
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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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WSNMF: Weighted Symmetric Nonnegative Matrix Factorization for Attributed Graph Clustering

Neurocomputing
In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph clustering. Nevertheless, when applied to attributed graph clustering, it confronts notable challenges.
Razieh Sheikhpour, Yuefeng Li, Kamal Berahmand   +2 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, Andrzej Cichocki, Guoxu Zhou   +2 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
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