Group Based Unsupervised Feature Selection

Abstract

Unsupervised feature selection is an important task in machine learning applications, yet challenging due to the unavailability of class labels. Although a few unsupervised methods take advantage of external sources of correlations within feature groups in feature selection, they are limited to genomic data, and suffer poor accuracy because they ignore input data or encourage features from the same group. We propose a framework which facilitates unsupervised filter feature selection methods to exploit input data and feature group information simultaneously, encouraging features from different groups. We use this framework to incorporate feature group information into Laplace Score algorithm. Our method achieves high accuracy compared to other popular unsupervised feature selection methods (sim 30% maximum improvement of Normalized Mutual Information (NMI)) with low computational costs (sim 50 times lower than embedded methods on average). It has many real world applications, particularly the ones that use image, text and genomic data, whose features demonstrate strong group structures.