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Outlier detection with partial information: application to emergency mapping
August 2008
Dalimonte2008OutlierDetection
This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known ‘background’ process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the probability density function, pdf) of the data generated by the ‘background’ process. The relative proportion of this ‘background’ component (the prior ‘background’ probability), the pdf and the prior probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known ‘background’ distribution.
Journal
Davide D’Alimonte, Dan Cornford
Stochastic Environmental Research and Risk Assessment
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22
5
613-620
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Export formats
Davide D’Alimonte and Dan Cornford, Outlier detection with partial information: application to emergency mapping, Stochastic Environmental Research and Risk Assessment, Vol. 22, No. 5, Pag. 613-620, August 2008.
<b><a href="/people/members/view.php?code=24baa59749868c7df6fcd822a5164196" class="author">Davide D’Alimonte</a> and Dan Cornford</b>, <u>Outlier detection with partial information: application to emergency mapping</u>, Stochastic Environmental Research and Risk Assessment, Vol. 22, No. 5, Pag. 613-620, August 2008.
@article {Dalimonte2008OutlierDetection, author = {Davide D’Alimonte and Dan Cornford}, title = {Outlier detection with partial information: application to emergency mapping}, journal = {Stochastic Environmental Research and Risk Assessment}, volume = {22}, number = {5}, pages = {613-620}, abstract = {This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known ‘background’ process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the probability density function, pdf) of the data generated by the ‘background’ process. The relative proportion of this ‘background’ component (the prior ‘background’ probability), the pdf and the prior probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known ‘background’ distribution.}, month = {August}, year = {2008}, }
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