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Lookup NU author(s): Dr Chunzheng Cao,
Dr Jian Shi
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2013 IEEE.We propose a robust task learning method based on nonlinear regression model with mixtures of t -distributions. The model can adaptively reduce the effects of complex noises and accurately learn the nonlinear structure of targets. By introducing latent variables, the model is expressed into a hierarchical structure, which helps explain the advantage of flexibility compared to the traditional Gaussian based learning model. We develop a two-stage efficient estimation procedure to obtain penalized likelihood estimator of the parameters combined an expectation-maximization algorithm with Lagrange multiplier method. The learning performances of the model are investigated through experiments on both synthetic and real data sets.
Author(s): Cao C, Wang Z, Shi JQ, Chen Y
Publication type: Article
Publication status: Published
Journal: IEEE Access
Online publication date: 10/06/2020
Acceptance date: 03/06/2020
Date deposited: 09/12/2020
ISSN (electronic): 2169-3536
Publisher: Institute of Electrical and Electronics Engineers Inc.
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