Problematic projection to the in-sample subspace for a kernelized anomaly detector

Journal Article · Sun Mar 06 23:00:00 EST 2016 · IEEE Geoscience and Remote Sensing Letters

DOI:https://doi.org/10.1109/LGRS.2016.2516985· OSTI ID:1247669

Theiler, James ^[1]; Grosklos, Guen ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. Here, we show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a wide range of bandwidths.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1247669
• Report Number(s): LA-UR--15-25934
• Journal Information: IEEE Geoscience and Remote Sensing Letters, Journal Name: IEEE Geoscience and Remote Sensing Letters Journal Issue: 4 Vol. 13; ISSN 1545-598X
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
adaptive signal detection
algorithms
anomaly detection
covariance matrices
data models
detectors
kernel density estimation
kernel-RX
mahalanobis distance
multidimensional signal processing
pattern recognition
remote sensing
singular value decomposition
spectral analysis