Abstract
A change detection algorithm for multi-dimensional data reduces the input space to a single statistic and compares it with a threshold to signal change. This study investigates the performance of two methods for estimating such a threshold: bootstrapping and control charts. The methods are tested on a challenging dataset of emotional facial expressions, recorded in real-time using Kinect for Windows. Our results favoured the control chart threshold and suggested a possible benefit from using multiple detectors. © 2014 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Faithfull, W. J., & Kuncheva, L. I. (2014). On optimum thresholding of multivariate change detectors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8621 LNCS, pp. 364–373). Springer Verlag. https://doi.org/10.1007/978-3-662-44415-3_37
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