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
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Information theoretic feature extraction for audio-visual speech recognition
IEEE Transactions on Signal Processing
Learning Bayesian networks with combination of MRMR criterion and EMI method
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
On numerical optimization theory of infinite kernel learning
Journal of Global Optimization
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, we propose a feature selection method based on a recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis basedmRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets.