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Optimizing Quantization for Lasso Recovery
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DOI：10.1109/LSP.2017.2770018.png)
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摘要
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This letter is focused on quantized compressed sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we propose a constrained Lloyd-Max-like framework to optimize the quantization function in this setting, and 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.
Keyword:
Nonlinear Lasso
quantized compressed sensing (CS)
quantization
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期刊
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9.6
论文数:
1.1W
被引数:
1.7W
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