A deep learning approach to structured signal recovery

Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

167 Scopus citations

Abstract

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.

Original languageEnglish (US)
Title of host publication2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1336-1343
Number of pages8
ISBN (Print)9781509018239
DOIs
StatePublished - Apr 4 2016
Event53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 - Monticello, United States
Duration: Sep 29 2015Oct 2 2015

Other

Other53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
CountryUnited States
CityMonticello
Period9/29/1510/2/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Systems Engineering

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