2011
NeurIPS
NeurIPS 2011
On the accuracy of l1-filtering of signals with block-sparse structure
Abstract
We discuss new methods for the recovery of signals with block-sparse structure, based on l1-minimization. Our emphasis is on the efficiently computable error bounds for the recovery routines. We optimize these bounds with respect to the method parameters to construct the estimators with improved statistical properties. We justify the proposed approach with an oracle inequality which links the properties of the recovery algorithms and the best estimation performance.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Compressed Sensing
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Core Methods > Feature Selection
Machine Learning > Optimization & Theory > Statistics
Mathematics & Optimization > Optimization > Sparse Optimization
Mathematics & Optimization > Optimization > Compressed Sensing
Mathematics & Optimization > Mathematics > Signal Processing
Machine Learning > Core Methods > Sparse Coding