Optimizing Quantization for Lasso Recovery
| Autor: | Deanna Needell, Shenyinying Tu, Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Yaniv Plan |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Science - Information Theory Information Theory (cs.IT) Applied Mathematics Quantization (signal processing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Data_CODINGANDINFORMATIONTHEORY 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences 94A12 60D05 90C25 Quantization (physics) Compressed sensing Lasso (statistics) Signal Processing 0202 electrical engineering electronic engineering information engineering 0101 mathematics Electrical and Electronic Engineering Algorithm |
| Zdroj: | IEEE Signal Processing Letters. 25:45-49 |
| ISSN: | 1558-2361 1070-9908 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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