Interactive Media Systems, TU Vienna

Evaluation of Robust PCA for Supervised Audio Outlier Detection

By Sarka Brodinova, Thomas Ortner, Peter Filzmoser, Maia Zaharieva, and Christian Breiteneder

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

S. Brodinova, T. Ortner, P. Filzmoser, M. Zaharieva, C. Breiteneder: "Evaluation of Robust PCA for Supervised Audio Outlier Detection"; Report No. CS-2015-2, 2015; 18 pages.

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