Convergence of a Fixed-Point Minimum Error Entropy Algorithm
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School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
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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
Entropy 2015, 17(8), 5549-5560; https://doi.org/10.3390/e17085549
Received: 3 May 2015 / Revised: 17 July 2015 / Accepted: 28 July 2015 / Published: 3 August 2015
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.
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Keywords:
information theoretic learning (ITL); minimum error entropy (MEE) criterion; fixed-point algorithm
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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.
AMA Style
Zhang Y, Chen B, Liu X, Yuan Z, Principe JC. Convergence of a Fixed-Point Minimum Error Entropy Algorithm. Entropy. 2015; 17(8):5549-5560.
Chicago/Turabian StyleZhang, Yu; Chen, Badong; Liu, Xi; Yuan, Zejian; Principe, Jose C. 2015. "Convergence of a Fixed-Point Minimum Error Entropy Algorithm" Entropy 17, no. 8: 5549-5560.
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