Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting
, 2017 Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require matrix-inversion; and (ii ...
Kasai, Takuma, Ono, Shunsuke
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Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis. [PDF]
BMC Bioinformatics, 2023 Rao L, Peng B, Li T.
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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
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Measuring the Effect of Vision on the Synergy of Lower Extremity Muscles during Walking using Nonnegative Matrix Factorization (NNMF) Algorithm Method. [PDF]
Appl Bionics Biomech, 2023 Safari N +4 more
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Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering. [PDF]
Inf Sci (N Y), 2022 Peng C +4 more
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Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization. [PDF]
J Imaging, 2021 Fernsel P.
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Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization. [PDF]
J Imaging, 2021 Dröge H +5 more
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Semantic Non-Negative Matrix Factorization for Term Extraction
Big Data and Cognitive ComputingThis study introduces an unsupervised term extraction approach that combines non-negative matrix factorization (NMF) with word embeddings. Inspired by a pioneering semantic NMF method that employs regularization to jointly optimize document–word and word–
Aliya Nugumanova +4 more
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