Results 21 to 30 of about 135,346 (336)
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 ...
Ge, Rong, Zou, James
core +3 more sources
Fast optimization of non-negative matrix tri-factorization.
PLoS ONE, 2019 Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses ...
Andrej Čopar +2 more
doaj +5 more sources
Non-negative matrix factorization with sparseness constraints
Journal of Machine Learning Research, 2004 Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based ...
Patrik O. Hoyer, Peter Dayan
core +4 more sources
Shifted Non-negative Matrix Factorization [PDF]
2007 IEEE Workshop on Machine Learning for Signal Processing, 2007 Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be ...
Hansen, Lars Kai +2 more
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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 +1 more source
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
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Parallel Non-Negative Matrix Tri-Factorization for Text Data Co-Clustering
IEEE Transactions on Knowledge and Data Engineering, 2023 As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real ...
Yufu Chen +6 more
semanticscholar +1 more source
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