Distributed SVM Applied to Image Classification

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Distributed SVM Applied to Image Classification
This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of atoms that spans the space of the observed signal. Redundant dictionaries allow for sparse representation of the observed signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise.
Effrosini Kokiopoulou, Pascal Frossard
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Authors Effrosini Kokiopoulou, Pascal Frossard
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