NDLI: Outlier robust kernel-based system identification using ℓ1-Laplace techniques

Content Provider	IEEE Xplore Digital Library
Author	Bottegal, Giulio Hjalmarsson, Hakan Aravkin, Alexandr Y. Pillonetto, Gianluigi
Copyright Year	2015
Description	Author affiliation: Department of Information Engineering, University of Padova, Italy (Pillonetto, Gianluigi) \|\| ACCESS Linnaeus Center, School of Electrical Engineering, KTH Royal Institute of Technology, Sweden (Bottegal, Giulio; Hjalmarsson, Hakan) \|\| IBM T.J. Watson Research Center, Yorktown Heights, NY, USA (Aravkin, Alexandr Y.)
Abstract	Regularized kernel-based methods for system identification have gained popularity in recent years. However, current formulations are not robust with respect to outliers. In this paper, we study possible solutions to robustify kernel-based methods that rely on modeling noise using the Laplacian probability density function (pdf). The contribution of this paper is two-fold. First, we introduce a new outlier robust kernel-based system identification method. It exploits the representation of Laplacian pdfs as scale mixture of Gaussians. The hyperparameters characterizing the problem are chosen using a new maximum a posteriori estimator whose solution is computed using a novel iterative scheme based on the expectation-maximization method. The second contribution of the paper is the review of two other robust kernel-based methods. The three methods are compared by means of numerical experiments, which show that all of them give substantial performance improvements compared to standard kernel-based methods for linear system identification.
Starting Page	2109
Ending Page	2114
File Size	282965
Page Count	6
File Format	PDF
e-ISBN	9781479978861
DOI	10.1109/CDC.2015.7402518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-15
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Kernel Laplace equations Standards Splines (mathematics) Probability density function Bayes methods
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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