Results 11 to 20 of about 16,408 (214)
Link prediction based on non-negative matrix factorization. [PDF]
PLoS ONE, 2017 With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like ...
Bolun Chen +4 more
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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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Scalable non-negative matrix tri-factorization
BioData Mining, 2017 Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining.
Andrej Čopar +2 more
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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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A non-convex optimization framework for large-scale low-rank matrix factorization
Machine Learning with Applications, 2022 Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in
Sajad Fathi Hafshejani +3 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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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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Optimization and expansion of non-negative matrix factorization
BMC Bioinformatics, 2020 Background Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics.
Xihui Lin, Paul C. Boutros
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Non-negative Matrix Factorization Parallel Optimization Algorithm Based on Lp-norm [PDF]
Jisuanji kexueNon-negative matrix factorization algorithm is an important tool for image clustering,data compression and feature extraction.Traditional non-negative matrix factorization algorithms mostly use Euclidean distance to measure reconstruction error,which has
HUANG Lulu, TANG Shuyu, ZHANG Wei, DAI Xiangguang
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