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Point process models for novelty detection on spatial point patterns and their extremes

Abstract:

Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of “normal behaviour”. The classification of point patterns is considered that are defined as sets of N observations of a multivariate random variable X and where the value N follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length N as well as the geometrical configuration in data space of such patterns. It ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.csda.2018.03.019

Authors


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Role:
Author
ORCID:
0000-0002-6781-7870
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Role:
Author
ORCID:
0000-0003-1023-3927
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Balliol College
Role:
Author
Flanders Research Foundation More from this funder
Royal Academy of Engineering More from this funder
Publisher:
Elsevier Publisher's website
Journal:
Computational Statistics and Data Analysis Journal website
Volume:
125
Pages:
86-103
Publication date:
2018-04-09
Acceptance date:
2018-03-30
DOI:
EISSN:
1872-7352
ISSN:
0167-9473
Source identifiers:
844784
Keywords:
Pubs id:
pubs:844784
UUID:
uuid:4ca6f459-e0fd-4a70-b44c-984d3d89fe10
Local pid:
pubs:844784
Deposit date:
2018-09-01

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