Results 21 to 30 of about 112,822 (316)
Rank selection for non‐negative matrix factorization
Statistics in Medicine, 2023 Non‐Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non‐negative data matrix into two lower dimensional non‐negative matrices: one is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the projections of data points to the new basis.
Yun Cai, Hong Gu, Toby Kenney
openaire +3 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 +6 more
doaj +1 more source
On affine non-negative matrix factorization [PDF]
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007 We generalize the non-negative matrix factorization (NMF) generative model to incorporate an explicit offset. Multiplicative estimation algorithms are provided for the resulting sparse affine NMF model. We show that the affine model has improved uniqueness properties and leads to more accurate identification of mixing and sources.
Laurberg, Hans, Hansen, Lars Kai
openaire +4 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. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated ...
Naiyang Guan +4 more
openaire +4 more sources
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
, 2015 Non-negative matrix factorization (NMF) is a natural model of admixture and is widely used in science and engineering. A plethora of algorithms have been developed to tackle NMF, but due to the non-convex nature of the problem, there is little guarantee ...
Rong Ge, James Zou
openalex +4 more sources
Probabilistic Non-Negative Matrix Factorization with Binary Components
Mathematics, 2021 Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data.
Xindi Ma +4 more
doaj +1 more source
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
openaire +4 more sources
Non-negative Matrix Factorization: A Survey
The Computer Journal, 2021 AbstractNon-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 storage space.
Gan J, Liu T, Li L, Zhang J
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source