Relative novelty detection

Alex J. Smola; Le Song; Choon Hui Teo

Relative novelty detection

Alex J. Smola^*, Le Song, Choon Hui Teo

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

51 Citations (Scopus)

Abstract

Novelty detection is an important tool for unsupervised data analysis. It relies on finding regions of low density within which events are then flagged as novel. By design this is dependent on the underlying measure of the space. In this paper we derive a formulation which is able to address this problem by allowing for a reference measure to be given in the form of a sample from an alternative distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.

Original language	English
Pages (from-to)	536-543
Number of pages	8
Journal	Journal of Machine Learning Research
Volume	5
Publication status	Published - 2009
Event	12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 - Clearwater, FL, United States Duration: 16 Apr 2009 → 18 Apr 2009

Cite this

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title = "Relative novelty detection",

abstract = "Novelty detection is an important tool for unsupervised data analysis. It relies on finding regions of low density within which events are then flagged as novel. By design this is dependent on the underlying measure of the space. In this paper we derive a formulation which is able to address this problem by allowing for a reference measure to be given in the form of a sample from an alternative distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.",

author = "Smola, {Alex J.} and Le Song and Teo, {Choon Hui}",

year = "2009",

language = "English",

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journal = "Journal of Machine Learning Research",

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note = "12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 ; Conference date: 16-04-2009 Through 18-04-2009",

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TY - JOUR

T1 - Relative novelty detection

AU - Smola, Alex J.

AU - Song, Le

AU - Teo, Choon Hui

PY - 2009

Y1 - 2009

N2 - Novelty detection is an important tool for unsupervised data analysis. It relies on finding regions of low density within which events are then flagged as novel. By design this is dependent on the underlying measure of the space. In this paper we derive a formulation which is able to address this problem by allowing for a reference measure to be given in the form of a sample from an alternative distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.

AB - Novelty detection is an important tool for unsupervised data analysis. It relies on finding regions of low density within which events are then flagged as novel. By design this is dependent on the underlying measure of the space. In this paper we derive a formulation which is able to address this problem by allowing for a reference measure to be given in the form of a sample from an alternative distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.

UR - http://www.scopus.com/inward/record.url?scp=84855817673&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84855817673

SN - 1532-4435

VL - 5

SP - 536

EP - 543

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

T2 - 12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009

Y2 - 16 April 2009 through 18 April 2009

ER -

Relative novelty detection

Abstract

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