# Compressed Learning : Universal Sparse Dimensionality Reduction and Learning in the Measurement Domain

@inproceedings{Calderbank2009CompressedL, title={Compressed Learning : Universal Sparse Dimensionality Reduction and Learning in the Measurement Domain}, author={Robert Calderbank}, year={2009} }

In this paper, we provide theoretical results to 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…

## 164 Citations

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### Compressed Classification from Learned Measurements

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Quantitative results demonstrated on CIFAR-10 image dataset show that the proposed framework provides better classification performance and robustness to noise compared to the tested state of the art deep compressed learning approaches.

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