Lab Home | Phone | Search | ||||||||
|
||||||||
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 |