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An extreme function theory for novelty detection

Abstract:

We introduce an extreme function theory as a novel method by which probabilistic novelty detection may be performed with functions, where the functions are represented by time-series of (potentially multivariate) discrete observations. We set the method within the framework of Gaussian processes (GP), which offers a convenient means of constructing a distribution over functions. Whereas conventional novelty detection methods aim to identify individually extreme data points, with respect to a ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/JSTSP.2012.2234081

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Centre for Statistics in Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More from this funder
Funding agency for:
Clifton, D
Grant:
WT 088877/Z/09/Z
More from this funder
Funding agency for:
Clifton, D
Grant:
WT 088877/Z/09/Z
Publisher:
IEEE Publisher's website
Journal:
IEEE Journal of selected topics in signal processing Journal website
Volume:
7
Issue:
1
Pages:
28-37
Publication date:
2012-12-13
DOI:
EISSN:
1941-0484
ISSN:
1932-4553
Source identifiers:
388558
Keywords:
Pubs id:
pubs:388558
UUID:
uuid:6c9ac841-e0e3-44fc-8ebb-324a363075fc
Local pid:
pubs:388558
Deposit date:
2013-11-17

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