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Nearly-optimal bounds for sparse recovery in generic norms, with applications to k-median sketching
[article]
2015
arXiv
pre-print
We initiate the study of trade-offs between sparsity and the number of measurements in sparse recovery schemes for generic norms. Specifically, for a norm ·, sparsity parameter k, approximation factor K>0, and probability of failure P>0, we ask: what is the minimal value of m so that there is a distribution over m × n matrices A with the property that for any x, given Ax, we can recover a k-sparse approximation to x in the given norm with probability at least 1-P? We give a partial answer to
arXiv:1504.01076v1
fatcat:jxyastg24nbi7dxmicdddwogha