@inproceedings{95cfa483552e40b7ac08a720b4f436b0,
title = "Efficient estimation of compressible state-space models with application to calcium signal deconvolution",
abstract = "In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nested Expectation-Maximization (EM) algorithms. Under suitable sparsity assumptions on the innovations, we prove recovery guarantees and derive confidence bounds for the state estimates. We provide simulation studies as well as application to spike deconvolution from calcium imaging data which verify our theoretical results and show significant improvement over existing algorithms.",
keywords = "Calcium imaging, Compressed sensing, Signal deconvolution, State-space models",
author = "Abbas Kazemipour and Ji Liu and Patric Kanold and Min Wu and Behtash Babadi",
note = "Funding Information: This work was supported in part by the National Institutes of Health Award No. R01DC009607 and the National Science Foundation Award No. 1552946. Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 ; Conference date: 07-12-2016 Through 09-12-2016",
year = "2017",
month = apr,
day = "19",
doi = "10.1109/GlobalSIP.2016.7906027",
language = "English (US)",
series = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1176--1180",
booktitle = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
}