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Algorithms for non-negative matrix factorization

D Seung, +1 more
- Vol. 13, pp 556-562
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The article was published on 2001-01-01 and is currently open access. It has received 5015 citations till now. The article focuses on the topics: Non-negative matrix factorization.

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Journal ArticleDOI

A majorization-minimization approach to the sparse generalized eigenvalue problem

TL;DR: The proposed sparse GEV algorithm, which offers a general framework to solve any sparse G EV problem, will give rise to competitive algorithms for a variety of applications where specific instances of GEV problems arise.
Book ChapterDOI

Label-Noise robust logistic regression and its applications

TL;DR: A label-noise robust version of the logistic regression and multinomiallogistic regression classifiers is considered and a novel sparsity-promoting regularisation approach is developed which allows us to tackle challenging high dimensional noisy settings.
Proceedings ArticleDOI

Nonnegative matrix factorizations as probabilistic inference in composite models

TL;DR: This paper describes multiplicative, Expectation-Maximization, Markov chain Monte Carlo and Variational Bayes algorithms for the NMF problem, and aims at providing statistical insights to NMF.
Journal ArticleDOI

Multitask Sparse Nonnegative Matrix Factorization for Joint Spectral–Spatial Hyperspectral Imagery Denoising

TL;DR: This paper proposes to solve the HSI denoising problem by sparse nonnegative matrix factorization (SNMF), which is an integrated model that combines parts-based dictionary learning and sparse coding, and shows that MTSNMF has superior performance on both synthetic and real-world data compared with several otherDenoising methods.
Proceedings ArticleDOI

Transfer learning to predict missing ratings via heterogeneous user feedbacks

TL;DR: This paper explores how to use the binary preference data expressed in the form of like/dislike to help reduce the impact of data sparsity of more expressive numerical ratings.
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