Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Image Feature Extraction by Sparse Coding and Independent Component Analysis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A New Method of Feature Extraction and Its Stability
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving better performances. In this paper, we propose a new feature extraction algorithm using independent component analysis (ICA) for classification problems. By using ICA in solving supervised classification problems, we can get new features which are made as independent from each other as possible and also convey the output information faithfully. Using the new features along with the conventional feature selection algorithms, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.