Survey on Probabilistic Models of Low-Rank Matrix Factorizations

Survey on Probabilistic Models of Low-Rank Matrix Factorizations

Low-rank matrix factorizations such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) are a large class of methods for pursuing the low-rank approximation of a given data matrix. The conventional factorization models are based on th...

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Bibliographic Details
Main Authors: Jiarong Shi, Xiuyun Zheng, Wei Yang
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Entropy
Subjects:
matrix factorizations
low-rank
variational Bayesian inference
Gibbs sampling
probabilistic principal component analysis
probabilistic matrix factorizations
probabilistic tensor factorizations
Online Access:https://www.mdpi.com/1099-4300/19/8/424

Internet

https://www.mdpi.com/1099-4300/19/8/424

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