Dimension Reduction by Mutual Information Feature Extraction
This work addresses feature extraction for high-dimensional data analysis, offering an incremental improvement in mutual information-based methods.
The paper tackles the challenge of accurate high-dimensional mutual information estimation for feature extraction by proposing a component-by-component gradient ascent method called MIFX, which achieves robust performance on UCI datasets, often being the best or comparable to the best methods.
During the past decades, to study high-dimensional data in a large variety of problems, researchers have proposed many Feature Extraction algorithms. One of the most effective approaches for optimal feature extraction is based on mutual information (MI). However it is not always easy to get an accurate estimation for high dimensional MI. In terms of MI, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on the target class and minimum redundancy. In this paper, a component-by-component gradient ascent method is proposed for feature extraction which is based on one-dimensional MI estimates. We will refer to this algorithm as Mutual Information Feature Extraction (MIFX). The performance of this proposed method is evaluated using UCI databases. The results indicate that MIFX provides a robust performance over different data sets which are almost always the best or comparable to the best ones.