Optimizing Matrices For Compressed Sensing Using Existing Goodness Measures: Negative Results, And An Alternative
Alankar Kotwal; Ajit Rajwade · 2017 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
The bound that arises out of sparse recovery analysis in compressed sensing involves input signal sparsity and some property of the sensing matrix. An effort has therefore been made in the literature to optimize sensing matrices for optimal recovery using this property. We discover, in the specific case of optimizing codes for the CACTI camera, that the popular method of mutual coherence minimization does not produce optimal results: codes designed to optimize effective dictionary coherence often perform worse than random codes in terms of mean squared reconstruction error. This surprising phe
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Metadata source: arXiv