Locally Linear Unsupervised Feature Selection

Guillaume DOQUET, Michèle SEBAG

Abstract: The paper, interested in unsupervised feature selection, aims to retain the features best accounting for the local patterns in the data. The proposed approach, called Locally Linear Unsupervised Feature Selection, relies on a dimensionality reduction method to characterize such patterns; each feature is thereafter assessed according to its compliance w.r.t. the local patterns, taking inspiration from Locally Linear Embedding (Roweis and Saul, 2000). The experimental validation of the approach on the scikit-feature benchmark suite demonstrates its effectiveness compared to the state of the art.

Keywords: Unsupervised Learning, Feature Selection, Dimension Reduction

TL;DR: Unsupervised feature selection through capturing the local linear structure of the data