Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors

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	Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors
	Luo, J.H.; Gan, Y.F.; Vong, C. M.; Wong, C.M.; Chen, C.Q.
	2021-10-01
Source Publication	Neurocomputing (SCI-E)
ISSN	0925-2312
Pages	106-116
Abstract	Sparse Bayesian learning (SBL) provides state-of-the-art performance in accuracy, sparsity and probabilistic prediction for classification. In SBL, the regularization priors are automatically determined that avoids an exhaustive hyperparameter selection by cross-validation. However, scalability to large problems is a drawback of SBL because of the inversion of a potentially enormous covariance matrix for updating the regularization priors in every iteration. This paper develops an approximate SBL algorithm called ARP-SBL, where the regularization priors are approximated without inverting the covariance matrix. Therefore, our approach can easily scale up to problems with large data size or feature dimension. It alleviates the long training time and high memory complexity in SBL. Based on ARP-SBL, two scalable nonlinear SBL models: scalable relevance vector machine (ARP-RVM) and scalable sparse Bayesian extreme learning machine (ARP-SBELM) are developed for problems of large data size and large feature size respectively. Experiments on a variety of benchmarks have shown that the proposed models are with competitive accuracy compared to existing methods while i) converging faster; ii) requiring thousands of times less memory; iii) without exhaustive regularized hyperparameter selection; and iv) easily scaling up to large data size and high dimensional features.
Keyword	Scalable Sparse Bayesian Learning Approximate Bayesian Regularization Priors Relevance Vector Machine Sparse Bayesian Extreme Learning Machine
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	58788
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Vong, C. M.
Recommended Citation GB/T 7714	Luo, J.H.,Gan, Y.F.,Vong, C. M.,et al. Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors[J]. Neurocomputing (SCI-E), 2021, 106-116.
APA	Luo, J.H.., Gan, Y.F.., Vong, C. M.., Wong, C.M.., & Chen, C.Q. (2021). Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors. Neurocomputing (SCI-E), 106-116.
MLA	Luo, J.H.,et al."Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors".Neurocomputing (SCI-E) (2021):106-116.

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