Transductive Nonnegative Matrix Tri-Factorization [PDF]
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
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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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Discriminative Multiview Nonnegative Matrix Factorization for Classification [PDF]
IEEE Access, 2019 Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination.
Weihua Ou +4 more
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Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization. [PDF]
J Imaging, 2021 Fernsel P.
europepmc +3 more sources
Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization. [PDF]
Int J Biomed Imaging, 2023 Sarrhini O +4 more
europepmc +3 more sources
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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A prognostic model related to necrotizing apoptosis of breast cancer based on biorthogonal constrained depth semi-supervised nonnegative matrix decomposition and single-cell sequencing analysis. [PDF]
Am J Cancer Res, 2023 Gao Y +6 more
europepmc +2 more sources
Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint Nonnegative Matrix Factorization. [PDF]
IEEE/ACM Trans Comput Biol Bioinform, 2020 Wang M +4 more
europepmc +3 more sources
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
doaj +1 more source
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
openaire +3 more sources