- Title
- Kernelized sparse Bayesian matrix factorization
- Creator
- Li, Caoyuan; Xie, Hong-Bo; Fan, Xuhui; Xu, Richard Yi Da; Van Huffel, Sabine; Mengersen, Kerrie
- Relation
- IEEE Transactions on Neural Networks and Learning Systems Vol. 32, Issue 1, p. 391-404
- Publisher Link
- http://dx.doi.org/10.1109/TNNLS.2020.2978761
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2021
- Description
- Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.
- Subject
- image restoration; low-rankness; matrix factorization; sparse Bayesian learning; variational Bayesian (VB) inference
- Identifier
- http://hdl.handle.net/1959.13/1443527
- Identifier
- uon:42024
- Identifier
- ISSN:2162-237X
- Language
- eng
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