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Randomized Nonnegative Matrix Factorization [PDF]
Pattern Recognition Letters, 2018 Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin +3 more
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Generalized Separable Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing.
Gillis, Nicolas, Pan, Junjun
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Sparse Deep Nonnegative Matrix Factorization [PDF]
Big Data Mining and Analytics, 2020 Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. However, deep learning networks, with their carefully designed hierarchical structure,
Zhenxing Guo, Shihua Zhang
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Computing a Nonnegative Matrix Factorization -- Provably [PDF]
SIAM Journal on Computing, 2011 In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m ...
Arora, Sanjeev +3 more
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Predicting epileptic seizures using nonnegative matrix factorization. [PDF]
PLoS ONE, 2020 This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial ...
Olivera Stojanović +2 more
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Monotonicity of the number of positive entries in nonnegative matrix powers [PDF]
Journal of Inequalities and Applications, 2018 Let A be a nonnegative matrix of order n and f(A) $f(A)$ denote the number of positive entries in A. We prove that if f(A)≤3 $f(A)\leq3$ or f(A)≥n2−2n+2 $f(A)\geq n^{2}-2n+2$, then the sequence {f(Ak)}k=1∞ $\{f(A^{k})\}_{k=1}^{\infty}$ is monotonic for ...
Qimiao Xie
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Descent methods for Nonnegative Matrix Factorization [PDF]
, 2008 In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method called Rank-one Residue Iteration (RRI).
Blondel, Vincent D. +2 more
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Coseparable Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and Applications, 2023 Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. It aims to find a low rank approximation for nonnegative data M by a product of two nonnegative matrices W and H. In general, NMF is NP-hard to solve while it can be solved efficiently under separability assumption, which requires the columns of factor matrix
Junjun Pan, Michael K. Ng
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Nonnegative low rank matrix approximation for nonnegative matrices [PDF]
Applied Mathematics Letters, 2020 25 pages 13 ...
Guang-Jing Song, Michael K. Ng
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