Robust Recognition via Information Theoretic Learning by Ran HeRobust Recognition via Information Theoretic Learning by Ran He

Robust Recognition via Information Theoretic Learning

byRan He, Baogang Hu, Xiaotong Yuan

Paperback | September 9, 2014

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This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.

The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.

Title:Robust Recognition via Information Theoretic LearningFormat:PaperbackDimensions:110 pagesPublished:September 9, 2014Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319074156

ISBN - 13:9783319074153

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Table of Contents

Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- ?1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.