Joint robust linear regression and anomaly detection in poisson noise using expectation-propagation

D. Yao, Y. Altmann, S. McLaughlin, M. E. Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In this paper, we propose a new Expectation-Propagation (EP) algorithm to address the problem of joint robust linear regression and sparse anomaly detection from data corrupted by Poisson noise. Adopting an approximate Bayesian approach, an EP method is derived to approximate the posterior distribution of interest. The method accounts not only for additive anomalies, but also for destructive anomalies, i.e., anomalies that can lead to observations with amplitudes lower than the expected signals. Experiments conducted with both synthetic and real data illustrate the potential benefits of the proposed EP method in joint spectral unmixing and anomaly detection in the photon-starved regime of a Lidar system.

Original languageEnglish
Title of host publication2020 28th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages2463-2467
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 18 Dec 2020
Event28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
https://eusipco2020.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

Conference28th European Signal Processing Conference
Abbreviated titleEUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period18/01/2122/01/21
Internet address

Keywords

  • Anomaly detection
  • Approximate Bayesian inference
  • Expectation-Propagation
  • Linear regression
  • Poisson noise

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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