Titlebook: Robust Recognition via Information Theoretic Learning; Ran He,Baogang Hu,Liang Wang Book 2014 The Author(s) 2014 Face recognition.informat

LVAD360

Correntropy with Nonnegative Constraint,introduction of an .. regularized nonnegative sparse coding algorithm to learn a nonnegative sparse representation (NSR). Then we show how to use correntropy to learn a robust NSR. Finally, based on the divide and conquer strategy, a two-stage framework is discussed for large-scale sparse representation problems.

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Introduction,ecognition. Despite significant improvement, performing robust classification is still challenging due to the nature of unpredictable outliers in an image. Outliers may occupy any parts of an image and have arbitrarily large values in magnitude [155].

Negligible

M-Estimators and Half-Quadratic Minimization,plays a role of Welsch M-estimator and can be efficiently optimized by half-quadratic minimization. Hence, in this chapter, we introduce some basic concepts of M-estimation and half-quadratic minimization.

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2191-5768 ion. 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

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Book 2014hods 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 a

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Book 2014iplicative 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..

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XML-Komponenten in der Praxis978-3-642-55619-7Series ISSN 1439-5428 Series E-ISSN 2522-0667

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0939-3145 brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the