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Paper Detail

Paper IDL.13.3
Paper Title An Efficient Running Quantile Estimation Technique alongside Correntropy for Outlier Rejection in Online Regression
Authors Sajjad Bahrami, Ertem Tuncel, University of California, Riverside, United States
Session L.13: Online, Active, and Transfer Learning
Presentation Lecture
Track Statistics and Learning Theory
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Abstract In this paper, online linear regression in the presence of non-Gaussian noise is addressed. In such environments, there are outliers in error samples (error between system output and labels) and/or the error does not follow a Gaussian distribution. Information theoretic measures such as error entropy criterion (EEC) and error correntropy criterion (ECC) are known for their superior performance compared to mean square error (MSE) in these cases. In this paper, an efficient technique of running quantile estimation based on quantization of error samples is introduced alongside which correntropy leads to lower steady state misalignment compared to previous algorithms.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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