A0391
Title: Evaluation of robust PCA for supervised audio outlier detection
Authors: Sarka Brodinova - Vienna University of Technology (Austria) [presenting]
Thomas Ortner - Vienna University of Technology (Austria)
Peter Filzmoser - Vienna University of Technology (Austria)
Maia Zaharieva - Vienna University of Technology (Austria)
Christian Breiteneder - Vienna University of Technology (Austria)
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 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.
