| Autor: |
Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi, Case, Mindy, Needell, Deanna, Plan, Yaniv |
| Předmět: |
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| Zdroj: |
IEEE Signal Processing Letters; Jan2018, Vol. 25 Issue 1, p45-49, 5p |
| Abstrakt: |
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. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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