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Your search keyword '"Chen, Badong"' showing total 2 results
2 results on '"Chen, Badong"'

1. Restricted Minimum Error Entropy Criterion for Robust Classification.

Author

Li, Yuanhao, Chen, Badong, Yoshimura, Natsue, and Koike, Yasuharu

Subjects
*RENYI'S entropy, *ENTROPY, *MACHINE learning, *SIGNAL processing
Abstract

The minimum error entropy (MEE) criterion is a powerful approach for non-Gaussian signal processing and robust machine learning. However, the instantiation of MEE on robust classification is a rather vacancy in the literature. The original MEE purely focuses on minimizing Renyi’s quadratic entropy of the prediction errors, which could exhibit inferior capability in noisy classification tasks. To this end, we analyze the optimal error distribution with adverse outliers and introduce a specific codebook for restriction, which optimizes the error distribution toward the optimal case. Half-quadratic-based optimization and convergence analysis of the proposed learning criterion, called restricted MEE (RMEE), are provided. The experimental results considering logistic regression and extreme learning machine on synthetic data and UCI datasets, respectively, are presented to demonstrate the superior robustness of RMEE. Furthermore, we evaluate RMEE on a noisy electroencephalogram dataset, so as to strengthen its practical impact. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Quantized minimum error entropy with fiducial points for robust regression.

Author

Zheng, Yunfei, Wang, Shiyuan, and Chen, Badong

Subjects
*ENTROPY, *REGRESSION analysis, *SAMPLING errors, *SIGNAL processing, *INSTRUCTIONAL systems
Abstract

Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data. [ABSTRACT FROM AUTHOR]

Published
2023
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