Evaluation of Robust PCA for Supervised Audio Outlier Detection

Evaluation of Robust PCA for Supervised Audio Outlier Detection

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.

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Authors
  • Brodinova, Sarka
  • Ortner, Thomas
  • Filzmoser, Peter
  • Zaharieva, Maia
  • Breiteneder, Christian
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Shortfacts
Category
Technical Report (Technical Report)
Divisions
Multimedia Information Systems
Subjects
Multimedia
Date
2015
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