Faculty of Informatics and Statistics, Department of Information and Knowledge Engineering (DIKE)

Date and time: March 9 2017 (16:00 – 17:30).

Room: 473 NB


Robust Classifiers in Multivariate Statistics and Machine Learning


  • Jan Kalina, ÚI AV ČR

Various methods multivariate statistics and data mining suffer from the presence of outlying measurements (outliers) in the data. Therefore, attention has been paid to proposing such robust alternatives which are resistant (insensitive) to data contamination. Two particular methods will be presented in the talk, namely (1) a robust version of support vector machine classifier, and (2) our proposal of a robust linear discriminant analysis. The latter method is also suitable for high-dimensional data with the number of variables exceeding the number of cases (observations). The talk is focused on the methodology and principles, but illustrative examples will be also presented.

Downloads: slides 1 

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