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Ran He & Baogang Hu 
Robust Recognition via Information Theoretic Learning 

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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.

€53.49
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Tabela de Conteúdo

Introduction.- M-estimators and Half-quadratic Minimization.- Information Measures.- Correntropy and Linear Representation.- ℓ1 Regularized Correntropy.- Correntropy with Nonnegative Constraint.
Língua Inglês ● Formato PDF ● Páginas 110 ● ISBN 9783319074160 ● Tamanho do arquivo 3.1 MB ● Editora Springer International Publishing ● Cidade Cham ● País CH ● Publicado 2014 ● Carregável 24 meses ● Moeda EUR ● ID 3350841 ● Proteção contra cópia DRM social

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