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Compressed Sensing by Gitta Kutyniok, Yonina C. Eldar

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10 Finding needles in compressed haystacks

Robert Calderbank and Sina Jafarpour

Abstract

In this chapter, we show that compressed learning, learning directly in the compressed domain, is possible. In particular, we provide tight bounds demonstrating that the linear kernel SVM’s classifier in the measurement domain, with high probability, has true accuracy close to the accuracy of the best linear threshold classifier in the data domain. We show that this is beneficial both from the compressed sensing and the machine learning points of view. Furthermore, we indicate that for a family of well-known compressed sensing matrices, compressed learning is provided on the fly. Finally, we support our claims with experimental results in the texture analysis ...

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