Robust Recognition via Information Theoretic Learning
Series: SpringerBriefs in Computer Science;
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Product details:
- Edition number 2014
- Publisher Springer International Publishing
- Date of Publication 9 September 2014
- Number of Volumes 1 pieces, Book
- ISBN 9783319074153
- Binding Paperback
- No. of pages110 pages
- Size 235x155 mm
- Weight 454 g
- Language English
- Illustrations XI, 110 p. 29 illus., 25 illus. in color. Illustrations, black & white 0
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Long description:
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.
MoreTable of Contents:
Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- l1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.
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