Unsupervised Feature Ranking and Selection Based on Autoencoders

Abstract

Feature selection is one of the most important and widely-used dimension reduction techniques due to its efficiency and intractability of the results. In this paper, we propose a simple but efficient unsupervised feature ranking and selection method by exploiting the geometry of the original feature space using AutoEncoders. Average reconstruction error of training samples by ignoring features, one at time, and the contribution of feature in the latent space (bottleneck of the auto-encoder) are proposed as two useful measures for ranking the features. The proposed method is evaluated for three different tasks: (1) feature selection, (2) discovering image interest points, and (3) extracting important blocks of an images Result on standard benchmarks confirm that the performance of our method is better than state-of-the-art methods.