Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Enhancements to the data mining process
Enhancements to the data mining process
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Kernel discriminant analysis for regression problems
Pattern Recognition
Hybrid structure for robust dimensionality reduction
Neurocomputing
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In manipulating data such as in supervised learning, we often extract new features from the original input variables for the purpose of reducing the dimensions of input space and achieving better performances. In this paper, we show how standard algorithms for independent component analysis (ICA) can be extended to extract attributes for regression problems. The advantage is that general ICA algorithms become available to a task of dimensionality reduction for regression problems by maximizing the joint mutual information between target variable and new attributes. We applied the proposed method to a couple of real world regression problems as well as some artificial problems and compared the performances with those of other conventional methods. Experimental results show that the proposed method can efficiently reduce the dimension of input space without degrading the regression performance.