Sparsity-Aware Learning and Compressed Sensing: An Overview

@article{Theodoridis2012SparsityAwareLA,
  title={Sparsity-Aware Learning and Compressed Sensing: An Overview},
  author={Sergios Theodoridis and Yannis Kopsinis and Konstantinos Slavakis},
  journal={CoRR},
  year={2012},
  volume={abs/1211.5231}
}
The notion of regularization has been widely used as a tool to address a number of problems that are usually encountered in Machine Learning. Improving the performance of an estimator by shrinking the norm of the MVU estimator, guarding against overfitting, coping with ill-conditioning, providing a solution to an underdetermined set of equations, are some notable examples where regularization has provided successful answers. A notable example is the ridge regression concept, where the LS loss… CONTINUE READING
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Design and analysis of hardware efficient compressed sensing architectures for compression in wireless sensors

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