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Entropy 2015, 17(8), 5549-5560; doi:10.3390/e17085549

Convergence of a Fixed-Point Minimum Error Entropy Algorithm

1
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
2
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
3
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 3 May 2015 / Revised: 17 July 2015 / Accepted: 28 July 2015 / Published: 3 August 2015
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Abstract

The minimum error entropy (MEE) criterion is an important learning criterion in information theoretical learning (ITL). However, the MEE solution cannot be obtained in closed form even for a simple linear regression problem, and one has to search it, usually, in an iterative manner. The fixed-point iteration is an efficient way to solve the MEE solution. In this work, we study a fixed-point MEE algorithm for linear regression, and our focus is mainly on the convergence issue. We provide a sufficient condition (although a little loose) that guarantees the convergence of the fixed-point MEE algorithm. An illustrative example is also presented. View Full-Text
Keywords: information theoretic learning (ITL); minimum error entropy (MEE) criterion; fixed-point algorithm information theoretic learning (ITL); minimum error entropy (MEE) criterion; fixed-point algorithm
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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MDPI and ACS Style

Zhang, Y.; Chen, B.; Liu, X.; Yuan, Z.; Principe, J.C. Convergence of a Fixed-Point Minimum Error Entropy Algorithm. Entropy 2015, 17, 5549-5560.

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