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Entropy 2018, 20(12), 968; https://doi.org/10.3390/e20120968

Semi-Supervised Minimum Error Entropy Principle with Distributed Method

1
and
2,*
1
School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China
2
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 26 October 2018 / Revised: 8 December 2018 / Accepted: 10 December 2018 / Published: 14 December 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
Full-Text   |   PDF [795 KB, uploaded 14 December 2018]   |  

Abstract

The minimum error entropy principle (MEE) is an alternative of the classical least squares for its robustness to non-Gaussian noise. This paper studies the gradient descent algorithm for MEE with a semi-supervised approach and distributed method, and shows that using the additional information of unlabeled data can enhance the learning ability of the distributed MEE algorithm. Our result proves that the mean squared error of the distributed gradient descent MEE algorithm can be minimax optimal for regression if the number of local machines increases polynomially as the total datasize. View Full-Text
Keywords: information theoretical learning; distributed method; MEE algorithm; semi-supervised approach; gradient descent; reproducing kernel Hilbert spaces information theoretical learning; distributed method; MEE algorithm; semi-supervised approach; gradient descent; reproducing kernel Hilbert spaces
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, B.; Hu, T. Semi-Supervised Minimum Error Entropy Principle with Distributed Method. Entropy 2018, 20, 968.

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