Speaker: Stéphane Canu (Professor at Normandie Université, France)
Title: Variable selection and outlier detection as a Mixed integer program (MIP)
Dimension reduction or feature selection is an effective strategy to handle contaminated data
and to deal with high dimensionality while providing better prediction.
To deal with outlier proneness and spurious variables, we propose a method performing the outright rejection of discordant observations together with the selection of relevant variables.
To solve this problem, it is recasted as a mixed integer program which allows the use of efficient commercial solver.
Also we propose an alternate projected gradient algorithm (proximal) so get a nice approximated solution.
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