Optimizing quantization for Lasso recovery
Information Theory
2016-06-10 v1 math.IT
Abstract
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantization function and show that the recovered signal converges to the actual signal at a quadratic rate as a function of the quantization level. We show that when the number of observations is high, this method of quantization gives a significantly better recovery rate than standard Lloyd-Max quantization. We support our theoretical analysis with numerical simulations.
Keywords
Cite
@article{arxiv.1606.03055,
title = {Optimizing quantization for Lasso recovery},
author = {Xiaoyi Gu and Shenyinying Tu and Hao-Jun Michael Shi and Mindy Case and Deanna Needell and Yaniv Plan},
journal= {arXiv preprint arXiv:1606.03055},
year = {2016}
}