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S. Brodinova, P. Filzmoser, T. Ortner, M. Zaharieva, C. Breiteneder:
"Robust and sparse clustering for high-dimensional data";
Vortrag: Conference of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), Milan, Italy; 13.09.2017 - 15.09.2017; in: "CLADAG 2017 Book of Short Papers", (2017), ISBN: 978-88-99459-71-0.

We introduce a robust and sparse clustering procedure for high-dimensional data. The robustness aspect is addressed by a weighting function incorporated in the k-means procedure, consequently leading to an automatic weight assignment for each observation. The sparsity aspect is given by a lasso-type penalty on weighted between-cluster sum of squares. We additionally propose a framework for determining the optimal number of both clusters and variables that contribute to a cluster separation.

We introduce a robust and sparse clustering procedure for high-dimensional data. The robustness aspect is addressed by a weighting function incorporated in the k-means procedure, consequently leading to an automatic weight assignment for each observation. The sparsity aspect is given by a lasso-type penalty on weighted between-cluster sum of squares. We additionally propose a framework for determining the optimal number of both clusters and variables that contribute to a cluster separation.

k-means clustering, outlier detection, high-dimensional data, variable selection, parameter selection

Projektleitung Maia Zaharieva:
Unusual sequences detection in very large video collections