Balson: Bayesian least squares optimization with nonnegative L1-Norm constraint

Xie, J; Ma, Z; Zhang, G; Xue, JH; Chien, JT; Lin, Z; Guo, J

Balson: Bayesian least squares optimization with nonnegative L1-Norm constraint

Xie, J Ma, Z Zhang, G

Xue, JH Chien, JT Lin, Z Guo, J

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Publication Type:: Conference Proceeding
Citation:: IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2018, 2018-September
Issue Date:: 2018-10-31

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Field	Value	Language
dc.contributor.author	Xie, J	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-4521-542X	en_US
dc.contributor.author	Xue, JH	en_US
dc.contributor.author	Chien, JT	en_US
dc.contributor.author	Lin, Z	en_US
dc.contributor.author	Guo, J	en_US
dc.date.available	2020-11-01T18:03:06Z
dc.date.issued	2018-10-31	en_US
dc.identifier.citation	IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2018, 2018-September	en_US
dc.identifier.isbn	9781538654774	en_US
dc.identifier.issn	2161-0363	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133124
dc.description.abstract	© 2018 IEEE. A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.	en_US
dc.relation.ispartof	IEEE International Workshop on Machine Learning for Signal Processing, MLSP	en_US
dc.relation.isbasedon	10.1109/MLSP.2018.8517036	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Balson: Bayesian least squares optimization with nonnegative L1-Norm constraint	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-September	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-September	en_US

Abstract:

© 2018 IEEE. A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.

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http://hdl.handle.net/10453/133124