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
Cite
@article{arxiv.1610.06461,
title = {Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution},
author = {Abbas Kazemipour and Ji Liu and Patrick Kanold and Min Wu and Behtash Babadi},
journal= {arXiv preprint arXiv:1610.06461},
year = {2016}
}
Comments
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec. 7-9, 2016, Washington D.C