Stability Comparison of Dimensionality Reduction Techniques Attending to Data and Parameter Variations

Abstract

The analysis of the big volumes of data requires efficient and robust dimension reduction techniques to represent data into lower-dimensional spaces, which ease human understanding. This paper presents a study of the stability, robustness and performance of some of these dimension reduction algorithms with respect to algorithm and data parameters, which usually have a major influence in the resulting embeddings. This analysis includes the performance of a large panel of techniques on both artificial and real datasets, focusing on the geometrical variations experimented when changing different parameters. The results are presented by identifying the visual weaknesses of each technique, providing some suitable data-processing tasks to enhance the stability.