Feature and kernel learning.

TL;DR: A survey of recent methods developed for feature selection/learning and their application to real world problems is provided, together with a review of the contributions to the ESANN 2015 special session on Feature and Kernel Learning.

Abstract: Feature selection and weighting has been an active research area in the last few decades nding success in many dierent applications. With the advent of Big Data, the adequate identication 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 se- lection 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 au- tomatically. 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. 1 Feature learning In the last few years, several datasets with high dimensionality have become publicly available on the Internet. This fact has brought an interesting challenge to the research community, since for the machine learning methods it is dicult to deal with a high number of input features. To cope with the problem of the high number of input features, dimensionality reduction techniques can be applied to reduce the dimensionality of the original data and improve learning performance. These dimensionality reduction techniques usually come in two