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Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio TransientsWe present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses 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 the method 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.
Document ID
20150006638
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Thompson, David R.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Majid, Walid A.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Reed, Colorado J.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Wagstaff, Kiri L.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
April 24, 2015
Publication Date
October 19, 2011
Subject Category
Astronomy
Astrophysics
Meeting Information
Meeting: Conference on Intelligent Data Understanding (CIDU 2011)
Location: Mountain View, CA
Country: United States
Start Date: October 19, 2011
End Date: October 21, 2011
Sponsors: Notre Dame Univ., NASA Headquarters, NASA Ames Research Center
Distribution Limits
Public
Copyright
Other
Keywords
anomaly detection
radio astronomy
machine learning
time series analysis
radio transients

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