University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Feature Selection by Filters: A Unifying Perspective

Feature Selection by Filters: A Unifying Perspective

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Per Kristian Lehre.

Feature Selection is an essential aspect of many fields – from computer vision, to data mining, to probabilistic modelling. The principle is to eliminate irrelevant or redundant variables from a dataset, given the requirement to predict a target. This has the dual advantage of reducing computation time, and increasing interpretability. Datasets with thousands to millions of variables require fast methods for selection—-these are known as “filters”. The last 15 years has seen a huge publication surge of candidate filter methods, with no common way to relate them or pick the right one for the right task. We focus on filters based on mutual information. This talk will give an overview of information theoretic methods, and present a recent unifying framework that shows the existence of a continous space of filters. Each paper over the last 15 years corresponds to a point in the space. Most of the space has never been explored. Based on recent work in AI-STATS 2009.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


Talks@bham, University of Birmingham. Contact Us | Help and Documentation | Privacy and Publicity.
talks@bham is based on from the University of Cambridge.