Results 11 to 20 of about 53,454 (335)

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   +6 more sources

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   +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, Tongliang Liu, Yangmuzi Zhang   +2 more
exaly   +6 more sources

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, Marinka žitnik, Blaž Zupan   +2 more
doaj   +4 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, Tong Liu, Li Li, Jilian Zhang   +3 more
semanticscholar   +3 more sources

Non-negative Matrix Factorization for Dimensionality Reduction [PDF]

ITM Web of Conferences, 2022
—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
doaj   +2 more sources

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
doaj   +2 more sources

Multi-constraint non-negative matrix factorization for community detection: orthogonal regular sparse constraint non-negative matrix factorization

Complex & Intelligent Systems
Community detection is an important method to analyze the characteristics and structure of community networks, which can excavate the potential links between nodes and further discover subgroups from complex networks.
Zigang Chen, Qi Xiao, Tao Leng, Zhenjiang Zhang, Ding Pan, Yuhong Liu, Xiaoyong Li   +6 more
doaj   +2 more sources

Guided Semi-Supervised Non-Negative Matrix Factorization

Algorithms, 2022
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li, Christine Tseng, Yaxuan Zheng, Joyce A. Chew, Longxiu Huang, Benjamin Jarman, Deanna Needell   +6 more
doaj   +2 more sources

Stretched non-negative matrix factorization

npj Computational Materials
A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge   +11 more
doaj   +3 more sources