Speaker
Kristina Jarůšková
(FNSPE CTU in Prague)
Description
When processing the HEP data it is often necessary to deal with the problem of high dimension of the dataset. Dimensionality reduction techniques represent a wise way of reducing the number of variables while preserving as much structure in the data as possible. This presentation will discuss the results of the implementation of a feature extraction method into the structure of a binary SDDT (supervised divergence decision tree).
Primary author
Kristina Jarůšková
(FNSPE CTU in Prague)
Peer reviewing
Paper
Paper files:
