Principles of multivariate analysis: a user's perspective
Principles of multivariate analysis: a user's perspective
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Variable Selection: Mutual Information and Linear Mixing Measures
IEEE Transactions on Knowledge and Data Engineering
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of the information by an adaptive partitioning of the observation space
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Maximization of Mutual Information for Supervised Linear Feature Extraction
IEEE Transactions on Neural Networks
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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This paper presents a novel approach for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on the target class. However, it is not always easy to get an accurate estimation for high-dimensional MI. In this paper, we propose an efficient method for feature extraction which is based on two-dimensional MI estimates. At each step, a new feature is created that attempts to maximize the MI between the new feature and the target class and to minimize the redundancy. We will refer to this algorithm as Minimax-MIFX. The effectiveness of the method is evaluated by using the classification of electroencephalogram (EEG) signals during hand movement imagination. The results confirm that the classification accuracy obtained by Minimax-MIFX is higher than that achieved by existing feature extraction methods and by full feature set.