Unsupervised relevance analysis for feature extraction and selection. A distance-based approach for feature relevance
Diego Hernán Peluffo-Ordóñez, John A. Lee, Michel Verleysen, José L. Rodríguez, German Castellanos-Dominguez · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2014
The aim of this paper is to propose a new generalized formulation for feature extraction based on distances from a feature relevance point of view. This is done within an unsupervised framework. To do so, it is first outlined the formal concept of feature relevance. Then, a novel feature extraction approach is introduced. Such an approach employs the M-norm as a distance measure. It is demonstrated that under some conditions, this method can readily explain literature methods. As another contribution of this paper, we propose an elegant feature ranking approach for feature selection followed from the spectral analysis of the data variability. Also, we provide a weighted PCA scheme revealing the relationship between feature extraction and feature selection. To assess the behavior of the studied methods within a pattern recognition system, a clustering stage is carried out. Normalized mutual information is used to quantify the quality of resultant clusters. Proposed methods reach comparable results with respect to literature methods.