An approach to outlier detection based on Bayesian probabilistic model

Victor L. Brailovsky*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The problem of outlier detection is considered with reference to a piecewise-smooth signal corrupted by background Gaussian noise plus spikes. The problem of estimating the variance of background noise is considered and a robust algorithm which solves the problem in such an environment is suggested. The estimate of variance is essential for an outlier detection algorithm as well as for different algorithms for signal (image) analysis. Our approach to outlier detection is based on a Bayesian probabilistic model. The model enables selection of a set of informative tests for outlier detection. An experimental algorithm based on this approach is tested and its comparison with the median based approach is presented.

Original languageEnglish
Title of host publicationTrack B
Subtitle of host publicationPattern Recognition and Signal Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-74
Number of pages5
ISBN (Print)081867282X, 9780818672828
DOIs
StatePublished - 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 25 Aug 199629 Aug 1996

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

Conference13th International Conference on Pattern Recognition, ICPR 1996
Country/TerritoryAustria
CityVienna
Period25/08/9629/08/96

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