Pages

Monday, May 13, 2019

A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices

** Nuit Blanche is now on Twitter: @NuitBlog **



In this paper we present a new algorithm for compressive sensing that makes use of binary measurement matrices and achieves exact recovery of ultra sparse vectors, in a single pass and without any iterations. Due to its noniterative nature, our algorithm is hundreds of times faster than ℓ1-norm minimization, and methods based on expander graphs, both of which require multiple iterations. Our algorithm can accommodate nearly sparse vectors, in which case it recovers index set of the largest components, and can also accommodate burst noise measurements. Compared to compressive sensing methods that are guaranteed to achieve exact recovery of all sparse vectors, our method requires fewer measurements However, methods that achieve statistical recovery, that is, recovery of almost all but not all sparse vectors, can require fewer measurements than our method.



Follow @NuitBlog or join the CompressiveSensing Reddit, the Facebook page, the Compressive Sensing group on LinkedIn  or the Advanced Matrix Factorization group on LinkedIn

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email.

Other links:
Paris Machine LearningMeetup.com||@Archives||LinkedIn||Facebook|| @ParisMLGroup About LightOnNewsletter ||@LightOnIO|| on LinkedIn || on CrunchBase || our Blog
About myselfLightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv

No comments:

Post a Comment