Compressed Sensing and Bayesian Experimental Design
author: Matthias W. Seeger, Laboratory for Probabilistic Machine Learning, École Polytechnique Fédérale de Lausanne
published: Aug. 29, 2008, recorded: July 2008, views: 9624
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study about linearly measuring natural images, we show that the simple standard heuristic of measuring Wavelet coefficients top-down systematically outperforms CS methods using random measurements; the sequential projection optimisation approach of [Ji & Carin 2007] performs even worse. We also show that our own approximate Bayesian method is able to learn measurement filters on full images efficiently which outperform the Wavelet heuristic. To our knowledge, ours is the first successful attempt at {}"learning compressed sensing" for images of realistic size. In contrast to common CS methods, our framework is not restricted to sparse signals, but can readily be applied to other notions of signal complexity or noise models. We give concrete ideas how our method can be scaled up to large signal representations.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: