Talks and Poster Presentations (with Proceedings-Entry):
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;
2016-08-23
- 2016-08-26; in: "Proceeding of 22nd International Conference on Computational Statistics (COMPSTAT)",
(2016),
ISBN: 978-90-73592-36-0;
12 pages.
English 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.
German 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.
Keywords:
Outlier detection, Robust PCA, Audio data, Experiments
Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_250911.pdf
Related Projects:
Project Head Maia Zaharieva:
Unusual sequences detection in very large video collections
Created from the Publication Database of the Vienna University of Technology.