Sensitivity to parameter and data variations in dimensionality reduction techniques

Dimensionality reduction techniques aim at representing high dimensional data in a meaningful and lower-dimensional space, improving the human comprehension and interpretation of data. In recent years, newer nonlinear techniques have been proposed in order to address the limitation of linear techniques. This paper presents a study of the stability of some of these dimensionality reduction techniques, analyzing their behavior under changes in the parameters and the data. The performances of these techniques are investigated on artificial datasets. The paper presents these results by identifying the weaknesses of each technique, and suggests some data-processing tasks to improve the stability