Results 21 to 30 of about 208,766 (283)
Empirical Bayes Matrix Factorization
Journal of machine learning research : JMLR, 2018 Matrix factorization methods - including Factor analysis (FA), and Principal Components Analysis (PCA) - are widely used for inferring and summarizing structure in multivariate data. Many matrix factorization methods exist, corresponding to different assumptions on the elements of the underlying matrix factors.
Wang, Wei, Stephens, Matthew
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Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering
International Journal of Computational Intelligence Systems, 2020 Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion.
Xiangguang Dai +3 more
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Sample Complexity of Dictionary Learning and other Matrix Factorizations [PDF]
, 2015 Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by ...
Bach, Francis +4 more
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Entropy Minimizing Matrix Factorization
IEEE Transactions on Neural Networks and Learning Systems, 2023 Nonnegative Matrix Factorization (NMF) is a widely-used data analysis technique, and has yielded impressive results in many real-world tasks. Generally, existing NMF methods represent each sample with several centroids, and find the optimal centroids by minimizing the sum of the approximation errors. However, the outliers deviating from the normal data
Mulin Chen, Xuelong Li
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Robust Probabilistic Matrix Tri-factorization
Jisuanji kexue yu tansuo, 2020 Matrix factorization is a commonly-used data analysis tool in computer vision, machine learning and data mining. In recent years, the probabilistic models of matrix factorization have become the focus of attention.
SHI Jiarong, CHEN Jiaojiao
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IEEE Access, 2021 Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of
Xuan Hu, Yongming Han, Zhiqiang Geng
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Negative Binomial Matrix Factorization [PDF]
IEEE Signal Processing Letters, 2020 We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF
Olivier Gouvert +2 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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BMC Bioinformatics, 2022 Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching
Junjun Zhang, Minzhu Xie
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Continuous Semi-Supervised Nonnegative Matrix Factorization
Algorithms, 2023 Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom +4 more
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