Results 11 to 20 of about 4,116 (162)
Sparse Deep Nonnegative Matrix Factorization [PDF]
Big Data Mining and Analytics, 2020 Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. However, deep learning networks, with their carefully designed hierarchical structure,
Zhenxing Guo, Shihua Zhang
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On a Guided Nonnegative Matrix Factorization [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features.
Joshua Vendrow +3 more
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Randomized nonnegative matrix factorization [PDF]
Pattern Recognition Letters, 2018 This is an extended and revised version of the paper which appeared in ...
N. Benjamin Erichson +3 more
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Cauchy nonnegative matrix factorization [PDF]
2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015 Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for nonnegative data. It enjoys a rich background, both from an optimization and probabilistic signal processing viewpoint. In this study, we propose a new cost-function for NMF fitting, which is introduced as arising naturally when adopting a Cauchy process model for ...
Liutkus, Antoine +2 more
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On Rationality of Nonnegative Matrix Factorization [PDF]
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017 Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. NMF has a wide variety of applications, including bioinformatics, chemometrics, communication complexity, machine learning, polyhedral combinatorics, among many others ...
Dmitry Chistikov 0001 +4 more
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Online Nonnegative Matrix Factorization With Outliers [PDF]
IEEE Transactions on Signal Processing, 2016 We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers.
Renbo Zhao, Vincent Y. F. Tan
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Nonnegative Unimodal Matrix Factorization
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry.
Ang, Man Shun +3 more
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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
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Transductive Nonnegative Matrix Tri-Factorization
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
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Robust Graph Regularized Nonnegative Matrix Factorization
IEEE Access, 2022 Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on
Qi Huang +3 more
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