The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of Rn. We examine the random projection method using artificial noisy image sequences as examples.
CITATION STYLE
Skubalska-Rafajłowicz, E. (2013). Random projections and hotelling’s t2 statistics for change detection in high-dimensional data streams. International Journal of Applied Mathematics and Computer Science, 23(2), 447–461. https://doi.org/10.2478/amcs-2013-0034
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