Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

author: Kiri L. Wagstaff, Machine Learning and Instrument Autonomy Group, Jet Propulsion Laboratory, California Institute of Technology (Caltech)
produced by: NASA Ames Video and Graphics Branch
published: June 27, 2012,   recorded: October 2011,   views: 2830

See Also:

Download slides icon Download slides: cidu2011_wagstaff_eigenbases_01.pdf (1.1┬áMB)

Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


We 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 e ective detection of interesting rare events and robustness to known false alarm anomalies.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: