Elements of information theory
Elements of information theory
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Conditional Mutual Information Based Feature Selection
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
Information theoretic feature extraction for audio-visual speech recognition
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Hypergraph spectra for semi-supervised feature selection
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Most existing feature selection methods focus on ranking features based on an information criterion to select the best K features. However, several authors find that the optimal feature combinations do not give the best classification performance [6],[5]. The reason for this is that although individual features may have limited relevance to a particular class, when taken in combination with other features it can be strongly relevant to the class. In this paper, we derive a new information theoretic criterion that called multidimensional interaction information (MII) to perform feature selection and apply it to gender determination. In contrast to existing feature selection methods, it is sensitive to the relations between feature combinations and can be used to seek third or even higher order dependencies between the relevant features. We apply the method to features delivered by principal geodesic analysis (PGA) and use a variational EM (VBEM) algorithm to learn a Gaussian mixture model for on the selected feature subset for gender determination. We obtain a classification accuracy as high as 95% on 2.5D facial needlemaps, demonstrating the effectiveness of our feature selection methods.