Results 1 to 10 of about 114,742 (232)

Fairer non-negative matrix factorization [PDF]

Frontiers in Big Data
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical
Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li   +5 more
doaj   +2 more sources

Discriminant projective non-negative matrix factorization. [PDF]

PLoS ONE, 2013
Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan, Xiang Zhang, Zhigang Luo, Dacheng Tao, Xuejun Yang   +4 more
doaj   +6 more sources

Co-sparse Non-negative Matrix Factorization [PDF]

Frontiers in Neuroscience, 2022
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu, Jiahui Cai, Canhong Wen, Haizhu Tan   +3 more
doaj   +2 more sources

In-memory analog computing for non-negative matrix factorization [PDF]

Nature Communications
Non-negative matrix factorization (NMF) is a powerful technique for extracting latent structures from high-dimensional data, with applications spanning recommender systems, bioinformatics, and image processing.
Shiqing Wang, Yubiao Luo, Pushen Zuo, Yongxiang Li, Lunshuai Pan, Daniele Ielmini, Zhong Sun   +6 more
doaj   +2 more sources

Graph regularized non-negative matrix factorization with $$L_{2,1}$$ L 2 , 1 norm regularization terms for drug–target interactions prediction [PDF]

BMC Bioinformatics, 2023
Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of
Junjun Zhang, Minzhu Xie
doaj   +2 more sources

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

NeuroImage, 2020
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to ...
Raihaan Patel, Christopher J. Steele, Anthony G.X. Chen, Sejal Patel, Gabriel A. Devenyi, Jürgen Germann, Christine L. Tardif, M. Mallar Chakravarty   +7 more
doaj   +2 more sources

JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification [PDF]

PeerJ
The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures.
Juan Liang, Jiuxi Huang, Chenxi Xi, Yun Wang, Juntao Li   +4 more
doaj   +3 more sources

Bayesian multi-study non-negative matrix factorization for mutational signatures [PDF]

Genome Biology
Mutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across ...
Isabella N. Grabski, Lorenzo Trippa, Giovanni Parmigiani   +2 more
doaj   +2 more sources

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation [PDF]

Entropy
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity.
Ling Zhong, Haiyan Gao
doaj   +2 more sources

Non-negative matrix factorization and deconvolution as a dual simplex problem [PDF]

Genome Biology
Background Non-negative matrix factorization is a powerful linear algebra tool used in multiple areas of data analysis, including computational biology.
Denis Kleverov, Ekaterina Aladyeva, Alexey Serdyukov, Maxim N. Artyomov   +3 more
doaj   +2 more sources