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 to reduce dimensionality and to reveal outliers. Thus, a thorough empirical evaluation of various PCA-based methods for the detection of outliers in a challenging audio data set is provided. The various experimental data settings are 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.
S. Brodinova, T. Ortner, P. Filzmoser, M. Zaharieva, C. Breiteneder: "Evaluation of robust PCA for supervised audio outlier detection"; Talk: 22nd International Conference on Computational Statistics (COMPSTAT), Oviedo, Spain; 08-23-2016 - 08-26-2016; in: "Proceeding of 22nd International Conference on Computational Statistics (COMPSTAT)", (2016), ISBN: 978-90-73592-36-0; 12 pages.
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