Recent methods for dimensionality reduction: A brief comparative analysis
Diego Hernán Peluffo-Ordóñez, John A. Lee, Michel Verleysen · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2014
Dimensionality reduction is a key stage for both the design of a pattern recognition system or data visualization. Recently, there has been a increasing interest in those methods aimed at preserving the data topology. Among them, Laplacian eigenmaps (LE) and stochastic neighbour embedding (SNE) are the most representative. In this work, we present a brief comparative among very recent methods being alternatives to LE and SNE. Comparisons are done mainly on two aspects: algorithm implementation, and complexity. Also, relations between methods are depicted. The goal of this work is providing researches on this field with some discussion as well as criteria decision to choose a method according to the user’s needs and/or keeping a good trade-off between performance and required processing time.