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E-book

Title Robust recognition via information theoretic learning / Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang
Published Cham : Springer, 2014
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Description 1 online resource (xi, 110 pages) : illustrations (some color)
Series SpringerBriefs in Computer Science, 2191-5768
SpringerBriefs in computer science. 2191-5768
Contents Introduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ℓ1 Regularized Correntropy -- Correntropy with Nonnegative Constraint
Summary 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. Theauthorsresort 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 briefintroduces 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
Bibliography Includes bibliographical references
Notes Online resource; title from PDF title page (SpringerLink, viewed September 10, 2014)
Subject Computer vision.
Machine learning.
Form Electronic book
Author He, Ran, author
Hu, Baogang, author
Yuan, Xiaotong, author
Wang, Liang, author
ISBN 9783319074160 (electronic bk.)
3319074164 (electronic bk.)
3319074156 (print)
9783319074153 (print)