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This Springer Short-term represents an entire analysis of data theoretic methods for robust recognition. Various information theoretic methods have been proffered in the earlier decade, in an enormous variety of laptop imaginative and prescient functions; this work brings them collectively, makes an try and impart the thought, optimization and utilization of data entropy.
The authors resort to a model new information theoretic concept, correntropy, as a robust measure and apply it to unravel robust face recognition and object recognition points. For computational effectivity, the short-term introduces the additive and multiplicative forms of half-quadratic optimization to successfully scale back entropy points and a two-stage sparse presentation framework for large scale recognition points. It moreover describes the strengths and deficiencies of varied robust measures in fixing robust recognition points.
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