Semi-Supervised Eigenbasis Novelty Detection

被引：0

作者：

Thompson, David R. ^{[1
]}

Majid, Walid A. ^{[1
]}

Reed, Colorado J. ^{[1
]}

Wagstaff, Kiri L. ^{[1
]}

机构：

[1] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA

来源：

STATISTICAL ANALYSIS AND DATA MINING | 2013年 / 6卷 / 03期

关键词：

novelty detection; time series analysis; radio astronomy; machine learning; anomaly detection; radio transients; fast transients; semi-supervised learning;

D O I：

10.1002/sam.11148

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses sparse, adaptive eigenbases to combine (1) prior knowledge about uninteresting signals with (2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply Semi-Supervised Eigenbasis Novelty Detection (SSEND) to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies. (c) 2012 Wiley Periodicals, Inc.

引用

页码：195 / 204

页数：10

共 22 条

[11]

Journée M, 2010, J MACH LEARN RES, V11, P517

[12]

Keane E., 2008, 3 ESTR WORKSH

[13]

Lazio J., 2009, DYNAMIC RADIO SKY OP

[14]

Lim Jongwoo., 2005, Advances in Neural Information Processing Systems, P793