A deep learning approach to structured signal recovery

Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
2015 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
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
more » ... 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.
doi:10.1109/allerton.2015.7447163 dblp:conf/allerton/MousaviPB15 fatcat:hv5dqtrzi5h53jphsepdkyw6fq