Results 11 to 20 of about 135,346 (336)
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
doaj +5 more sources
Feature Weighted Non-Negative Matrix Factorization [PDF]
IEEE Transactions on Cybernetics, 2021 Non-negative matrix factorization (NMF) is one of the most popular techniques for data representation and clustering and has been widely used in machine learning and data analysis.
Mulin Chen, Maoguo Gong, Xuelong Li
semanticscholar +6 more sources
Non-negative Matrix Factorization: A Survey
The Computer Journal, 2021 Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less ...
Jiangzhang Gan +3 more
semanticscholar +3 more sources
Truncated Cauchy Non-Negative Matrix Factorization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers.
Naiyang Guan +4 more
semanticscholar +6 more sources
Stretched non-negative matrix factorization
npj Computational MaterialsA novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu +11 more
doaj +3 more sources
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
doaj +4 more sources
Molecular subtyping of Alzheimer's disease with consensus non-negative matrix factorization. [PDF]
PLoS One, 2021 Alzheimer’s disease (AD) is a heterogeneous disease and exhibits diverse clinical presentations and disease progression. Some pathological and anatomical subtypes have been proposed. However, these subtypes provide a limited mechanistic understanding for
Zheng C, Xu R.
europepmc +2 more sources
Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization. [PDF]
Mathematics (Basel), 2021 Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m×n data matrix X into an m×k matrix W and a k×n matrix H, so that X≈W×H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF
Maisog JM +5 more
europepmc +2 more sources
Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug–target interactions prediction [PDF]
BMC Bioinformatics, 2022 Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching
Junjun Zhang, Minzhu Xie
doaj +2 more sources
Topic supervised non-negative matrix factorization [PDF]
, 2017 Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs.
MacMillan, Kelsey, Wilson, James D.
core +4 more sources