A Unifying Framework for Information Theoretic Feature Selection
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Thu, 17/05/2012 16:00 |
Gavin Brown (Manchester) |
Industrial and Applied Mathematics Seminar  |
DH 1st floor SR |
| Feature Selection is a ubiquitous problem in across data mining, bioinformatics, and pattern recognition, known variously as variable selection, dimensionality reduction, and others. Methods based on information theory have tremendously popular over the past decade, with dozens of 'novel' algorithms, and hundreds of applications published in domains across the spectrum of science/engineering. In this work, we asked the question 'what are the implicit underlying statistical assumptions of feature selection methods based on mutual information?' The main result I will present is a unifying probabilistic framework for information theoretic feature selection, bringing almost two decades of research on heuristic methods under a single theoretical interpretation. | |||