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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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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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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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
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
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Robust self supervised symmetric nonnegative matrix factorization to the graph clustering. [PDF]
Sci RepRu Y, Gruninger M, Dou Y.
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Robust Structured Convex Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2021 Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning. Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix.
Qing Yang +3 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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Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics
Mathematics, 2023 Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du +3 more
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