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Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data. [PDF]
J Med Signals SensMiri M, Sadeghi MT, Abootalebi V.
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An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation [PDF]
Symmetry, 2020 Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering. In this paper, we propose an accelerated variant of the multiplicative update (MU) algorithm of He et al ...
Zhaoshui He, Ji Tan, Beihai Tan
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Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and ApplicationsAbstract. Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose. To design faster and more scalable algorithms for SymNMF, we develop two randomized algorithms for its computation ...
Haesun Park +2 more
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Symmetric Nonnegative Matrix Factorization: A Systematic Review
Neurocomputing, 2023Wen-Sheng Chen, Binbin Pan
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Efficient Algorithm for Sparse Symmetric Nonnegative Matrix Factorization
Pattern Recognition Letters, 2019Abstract 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 Belachew
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A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization
Lecture Notes in Computer Science, 2018This 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 ...
Jun Wang, Hangjun Che
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Block-Diagonal Guided Symmetric Nonnegative Matrix Factorization
IEEE Transactions on Knowledge and Data Engineering, 2021Symmetric 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 +3 more
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