Zimos, E., Mota, J., Rodrigues, M., & Deligiannis, N. (2016). Bayesian Compressed Sensing with Heterogeneous Side Information. In Data Compression Conference (DCC), 2016 (pp. 1-10)

@inproceedings{8cd1e26edc9e492c9bfe3ea0f6673a90,
title = "Bayesian Compressed Sensing with Heterogeneous Side Information",
abstract = "The classical compressed sensing (CS) paradigm can be modi ed so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages to exploit side information generated from a diverse (or heterogeneous) data source by using the statistical model of copula functions. Through simulations, we demonstrate that the proposed method yields significant reduction in the mean-squared error of the reconstructed signal as compared to state-of-the-art methods in classical compressed sensing and compressed sensing with side information.",
keywords = "Compressed sensing, Bayes methods, Probability density function",
author = "Evangelos Zimos and Jo{\~a}o Mota and Miguel Rodrigues and Nikolaos Deligiannis",
year = "2016",
language = "English",
pages = "1--10",
booktitle = "Data Compression Conference (DCC), 2016",
note = "IEEE Data Compression Conference DCC 2016 ; Conference date: 29-03-2016",
}