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M-Estimators and Half-Quadratic Minimization

Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In robust statistics, there are several types of robust estimators, including M-estimator (maximum likelihood type estimator), L-estimator (linear combinations of order statistics), R-estimator (estimator based on rank transformation) [77], RM estimator (repeated median) [141], and LMS estimator (estimator using the least median of squares) [133]. When information theoretic learning is applied to robust statistics, the Gaussian kernel in entropy plays a role of Welsch M-estimator and can be efficiently optimized by half-quadratic minimization. Hence, in this chapter, we introduce some basic concepts of M-estimation and half-quadratic minimization.

Keywords

Minimization Function Additive Form Robust Statistic Multiplicative Form Repeated Median 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijingChina
  2. 2.School of Information and ControlNanjing University of Information Science and TechnologyNanjingChina

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