A Note on the W-S Lower Bound of the MEE Estimation
The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean squa...
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MDPI AG
2014-02-01
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Online Access: | http://www.mdpi.com/1099-4300/16/2/814 |
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author | Badong Chen Guangmin Wang Nanning Zheng Jose C. Principe |
author_facet | Badong Chen Guangmin Wang Nanning Zheng Jose C. Principe |
author_sort | Badong Chen |
collection | DOAJ |
description | The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated. |
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issn | 1099-4300 |
language | English |
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spelling | doaj.art-0b90ef5a5d5c40c5b867639abab79ca32022-12-22T03:59:34ZengMDPI AGEntropy1099-43002014-02-0116281482410.3390/e16020814e16020814A Note on the W-S Lower Bound of the MEE EstimationBadong Chen0Guangmin Wang1Nanning Zheng2Jose C. Principe3Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaInstitute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaInstitute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USAThe minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.http://www.mdpi.com/1099-4300/16/2/814estimationentropyMEE estimationW-S lower bound |
spellingShingle | Badong Chen Guangmin Wang Nanning Zheng Jose C. Principe A Note on the W-S Lower Bound of the MEE Estimation Entropy estimation entropy MEE estimation W-S lower bound |
title | A Note on the W-S Lower Bound of the MEE Estimation |
title_full | A Note on the W-S Lower Bound of the MEE Estimation |
title_fullStr | A Note on the W-S Lower Bound of the MEE Estimation |
title_full_unstemmed | A Note on the W-S Lower Bound of the MEE Estimation |
title_short | A Note on the W-S Lower Bound of the MEE Estimation |
title_sort | note on the w s lower bound of the mee estimation |
topic | estimation entropy MEE estimation W-S lower bound |
url | http://www.mdpi.com/1099-4300/16/2/814 |
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