Recent methods for dimensionality reduction: A brief comparative analysis

Dimensionality reduction is a key stage for both the design of a pat- tern recognition system or data visualization. Recently, there has been a increas- ing 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 meth- ods 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.