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Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces Read chapter Read first chapter

2010 | OriginalPaper | Chapter

10. Correntropy for Random Variables: Properties and Applications in Statistical Inference

Authors : Weifeng Liu, Puskal Pokharel, Jianwu Xu, Sohan Seth

Published in: Information Theoretic Learning

Publisher: Springer New York

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Abstract

Similarity is a key concept to quantify temporal signals or static measurements. Similarity is difficult to define mathematically, however, one never really thinks too much about this difficulty and naturally translates similarity by correlation. This is one more example of how engrained second-order moment descriptors of the probability density function really are in scientific thinking. Successful engineering or pattern recognition solutions from these methodologies rely heavily on the Gaussianity and linearity assumptions, exactly for the same reasons discussed in Chapter 3.

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previous chapter A Reproducing Kernel Hilbert Space Framework for ITL
next chapter Correntropy for Random Processes: Properties and Applications in Signal Processing
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Metadata
Title
Correntropy for Random Variables: Properties and Applications in Statistical Inference
Authors
Weifeng Liu
Puskal Pokharel
Jianwu Xu
Sohan Seth
Copyright Year
2010
Publisher
Springer New York
Book
Information Theoretic Learning

Print ISBN: 978-1-4419-1569-6

Electronic ISBN: 978-1-4419-1570-2

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-1-4419-1570-2_10

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