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.
@article{bwmeta1.element.bwnjournal-article-amcv23z2p447bwm,
author = {Ewa Skubalska-Rafaj\l owicz},
title = {Random projections and hotelling's T$^2$ statistics for change detection in high-dimensional data streams},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {23},
year = {2013},
pages = {447-461},
zbl = {1282.93064},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv23z2p447bwm}
}
Ewa Skubalska-Rafajłowicz. Random projections and hotelling's T² statistics for change detection in high-dimensional data streams. International Journal of Applied Mathematics and Computer Science, Tome 23 (2013) pp. 447-461. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv23z2p447bwm/
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