Abstract

Outliers often reveal crucial information about the underlying data such as the presence of unusual observations that require for in-depth analysis. The detection of outliers is especially challenging in real-world application scenarios dealing with high-dimensional and flat data bearing different subpopulations of potentially varying data distributions. In the context of high-dimensional data, PCA-based methods are commonly applied in order to reduce dimensionality and to reveal outliers. In this paper, we perform a thorough empirical evaluation of well-establish PCA-based methods for the detection of outliers in a challenging audio data set. In this evaluation we focus on various experimental data settings motivated by the requirements of real-world scenarios, such as varying number of outliers, available training data, and data characteristics in terms of potential subpopulations.

Reference

Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2015). Evaluation of Robust PCA for Supervised Audio Outlier Detection (CS-2015-2). http://hdl.handle.net/20.500.12708/38539

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