Relative Novelty Detection

Alex Smola, Le Song, Choon Hui Teo
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:536-543, 2009.

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 alternate distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-smola09a, title = {Relative Novelty Detection}, author = {Alex Smola and Le Song and Choon Hui Teo}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {536--543}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/smola09a/smola09a.pdf}, url = {http://proceedings.mlr.press/v5/smola09a.html}, 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 alternate distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.} }
Endnote
%0 Conference Paper %T Relative Novelty Detection %A Alex Smola %A Le Song %A Choon Hui Teo %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-smola09a %I PMLR %J Proceedings of Machine Learning Research %P 536--543 %U http://proceedings.mlr.press %V 5 %W PMLR %X 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 alternate distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.
RIS
TY - CPAPER TI - Relative Novelty Detection AU - Alex Smola AU - Le Song AU - Choon Hui Teo BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-smola09a PB - PMLR SP - 536 DP - PMLR EP - 543 L1 - http://proceedings.mlr.press/v5/smola09a/smola09a.pdf UR - http://proceedings.mlr.press/v5/smola09a.html 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 alternate distribution. We show that this optimization problem can be solved efficiently and that it works well in practice. ER -
APA
Smola, A., Song, L. & Teo, C.H.. (2009). Relative Novelty Detection. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:536-543

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