Non-negative matrix factorization test cases [PDF]
2016 IEEE MIT Undergraduate Research Technology Conference (URTC), 2016 4 pages, 3 figures, to appear in the proceedings of the 2015 IEEE MIT Undergraduate Research ...
Connor Sell, Jeremy Kepner
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Graph non-negative matrix factorization with alternative smoothed $$L_0$$ L 0 regularizations
Neural computing & applications (Print), 2022 Graph non-negative matrix factorization (GNMF) can discover the data’s intrinsic low-dimensional structure embedded in the high-dimensional space. So, it has superior performance for data representation and clustering.
Keyi Chen +3 more
semanticscholar +1 more source
Gene Expression Analysis through Parallel Non-Negative Matrix Factorization
Computation, 2021 Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed.
Angelica Alejandra Serrano-Rubio +2 more
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Mathematics, 2023 Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix ...
Minghua Wan, Mingxiu Cai, Guowei Yang
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Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19
Frontiers in Immunology, 2021 A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug
Xianfang Tang +5 more
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Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion [PDF]
Jisuanji kexue, 2021 An effective algorithm for unmixing hyperspectral and panchromatic images of non-negative matrix factorization based on spectral reconstruction constraint is proposed.Firstly,this algorithm employs the regularization with minimum spectral reconstruction ...
GUAN Zheng, DENG Yang-lin, NIE Ren-can
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Kernel Joint Non-Negative Matrix Factorization for Genomic Data
IEEE Access, 2021 The multi-modal or multi-view integration of data has generated a wide range of applicability in pattern extraction, clustering, and data interpretation.
Diego Salazar +4 more
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Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization
IEEE Access, 2018 Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) obtain precise models using model-based solutions; 3) get ...
Jesus Bobadilla +3 more
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Robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization
Mathematical Biosciences and Engineering, 2023 Non-negative matrix factorization (NMF) has been widely used in machine learning and data mining fields. As an extension of NMF, non-negative matrix tri-factorization (NMTF) provides more degrees of freedom than NMF.
Jiyang Yu +3 more
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Initialization for non-negative matrix factorization: a comprehensive review [PDF]
International Journal of Data Science and Analysis, 2021 Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix.
Sajad Fathi Hafshejani, Z. Moaberfard
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