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Thursday, June 23, 2016
2:30 PM - 2:40 PM
CNLS Conference Room (TA-3, Bldg 1690)

Student Seminar

A New Approach for Anomaly Detection: The Kernelization of Khachiyan's Algorithm

Guen Grosklos
Utah State University

In its standard form, the Khachiyan algorithm efficiently identifies the ellipsoid that encloses every point in a d-dimensional dataset -- it turns out that this is a convex optimization problem. By kernelizing this algorithm, we will be able to develop a more data-adaptive anomaly detector that pays more attention to the periphery of the data while also more effectively enclosing non-Gaussian and non-ellipsoidal data. This will be compared to the kernelized RX algorithm, a well known anomaly detector commonly used for anomaly detection in hyperspectral imaging.

Host: Chris Neale