Machine Learning Seminar presentation
Topic: Gaussian Process Regression with anisotropic kernel for supervised feature selection.
Speaker: Aubin Geoffre, École Mines de Saint-Étienne
Time: Wednesday, 2022.05.04, 10:00 CET
How to join: Please contact Jakub Lengiewicz
A method of feature selection based on Gaussian Process Regression has been proposed. It can be applied to a wide range of applications where the parameters that best explain a given output are sought among a set of correlated features. The method uses anisotropic kernel that associates a hyperparameter to each feature. These hyperparameters can be interpreted as correlation lengths providing an estimation of the weight of each feature w.r.t the output.
Video recording: https://youtu.be/S7_RcE2WmGk