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.

Reference

Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2016). Evaluation of robust PCA for supervised audio outlier detection. In Proceeding of 22nd International Conference on Computational Statistics (COMPSTAT) (p. 12). http://hdl.handle.net/20.500.12708/56525

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