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.