Connections between sparse estimation and robust statistical learning
2013 (English)In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE conference proceedings, 2013, 5489-5493 p.Conference paper (Refereed)
Recent literature on robust statistical inference suggests that promising outlier rejection schemes can be based on accounting explicitly for sparse gross errors in the modeling, and then relying on compressed sensing ideas to perform the outlier detection. In this paper,we consider two models for recovering a sparse signal from noisy measurements, possibly also contaminated with outliers. The models considered here are a linear regression model, and its natural onebit counterpart where measurements are additionally quantized to a single bit. Our contributions can be summarized as follows: We start by providing conditions for identification and the Cramer-Rao Lower Bounds (CRLBs) for these two models. Then, focusing on the one-bit model, we derive conditions for consistency of the associated Maximum Likelihood estimator, and show the performance of relevant ell1-based relaxation strategies by comparing against the theoretical CRLB.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 5489-5493 p.
, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149
Sparsity, robustness, outlier detection, Cram er- Rao lower bounds
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-136971DOI: 10.1109/ICASSP.2013.6638713ISI: 000329611505131ScopusID: 2-s2.0-84890485641ISBN: 978-1-4799-0356-6OAI: oai:DiVA.org:kth-136971DiVA: diva2:677624
2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013; Vancouver, BC; Canada; 26 May 2013 through 31 May 2013
QC 201312102013-12-102013-12-102014-02-25Bibliographically approved