Feature and kernel learning

Feature selection and weighting has been an active research area in the last few decades finding success in many different applications. With the advent of Big Data, the adequate identification of the relevant features has converted feature selection in an even more indispensable step. On the other side, in kernel methods features are implicitly represented by means of feature mappings and kernels. It has been shown that the correct selection of the kernel is a crucial task, as long as an erroneous selection can lead to poor performance. Unfortunately, manually searching for an optimal kernel is a time-consuming and a sub-optimal choice. This tutorial is concerned with the use of data to learn features and kernels automatically. We provide a survey of recent methods developed for feature selection/learning and their application to real world problems, together with a review of the contributions to the ESANN 2015 special session on Feature and Kernel Learning.