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Robust Task Learning Based on Nonlinear Regression with Mixtures of Student-t Distributions

Lookup NU author(s): Dr Chunzheng Cao, Dr Jian Shi

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 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.

Publication metadata

Author(s): Cao C, Wang Z, Shi JQ, Chen Y

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2020

Volume: 8

Pages: 109835-109844

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.

URL: https://doi.org/10.1109/ACCESS.2020.3001371

DOI: 10.1109/ACCESS.2020.3001371

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