Authors: Martijn Onderwater
Addresses: Department of Stochastics, Centre for Mathematics and Computer Science (CWI), Science Park 123, 1098 XG, Amsterdam, The Netherlands; Faculty of Sciences, VU University, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands
Abstract: Sensors are increasingly part of our daily lives: motion detection, lighting control, and energy consumption all rely on sensors. Combining this information into, for instance, simple and comprehensive graphs can be quite challenging. Dimensionality reduction is often used to address this problem, by decreasing the number of variables in the data and looking for shorter representations. However, dimensionality reduction is often aimed at normal daily data, and applying it to events deviating from this daily data (so-called outliers) can affect such events negatively. In particular, outliers might go unnoticed. In this paper, we show that dimensionality reduction can indeed have a large impact on outliers. To that end we apply three dimensionality reduction techniques to three real-world datasets, and inspect how well they preserve outliers. We use several performance measures to show how well these techniques are capable of preserving outliers, and we discuss the results.
Keywords: dimensionality reduction; outlier detection; multidimensional scaling; MDS; principal component analysis; PCA; peeling; F1-score; t-stochastic neighbourhood embedding; t-SNE; Matthews correlation; relative information score; sensor networks; outlier preservation; outliers; performance measures.
International Journal of Data Analysis Techniques and Strategies, 2015 Vol.7 No.3, pp.231 - 252
Published online: 22 Aug 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article