Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generative model-based clustering of directional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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We consider the issue of online anomaly detection from a time sequence of directional data (normalized vectors) in high dimensional systems. In spite of the practical importance, little is known about anomaly detection methods for directional data. Using a novel concept of the effective dimension of the system, we successfully formulated an anomaly detection method which is free from the "curse of dimensionality." In our method, we derive a probability distribution function (pdf) for an anomaly metric, and use a novel update algorithm for the parameters in the pdf, where the effective dimension is included as a fitting parameter. For directional data from a computer system, we demonstrate the utility of our algorithm in anomaly detection.