A Discrimination Analysis for Unsupervised Feature Selection via Optic Diffraction Principle
Abstract
This paper proposes an unsupervised discrimination analysis for feature selection based on a property of the Fourier transform of the probability density distribution. Each feature is evaluated on the basis of a simple observation motivated by the concept of optical diffraction, which is invariant under feature scaling. The time complexity is O(mn), where m is number of features and n is number of instances when being applied directly to the given data. This approach is also extended to deal with data orientation, which is the direction of data alignment. Therefore, the discrimination score of any transformed space can be used for evaluating the original features. The experimental results on several real-world datasets demonstrate the effectiveness of the proposed method.
Authors: Praisan Padungweang, Chidchanok Lursinsap, Khamron Sunat
Published in: IEEE Transactions on Neural Networks and Learning Systems (2012)