Stability Comparison of Dimensionality Reduction Techniques Attending to Data and Parameter Variations
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:PE.VAMP.VAMP2013.005-009,
booktitle = {EuroVis Workshop on Visual Analytics using Multidimensional Projections},
editor = {M. Aupetit and L. van der Maaten},
title = {{Stability Comparison of Dimensionality Reduction Techniques Attending to Data and Parameter Variations}},
author = {García-Fernández, Francisco J. and Verleysen, Michel and Lee, John A. and Díaz, Ignacio},
year = {2013},
publisher = {The Eurographics Association},
ISBN = {978-3-905674-53-8},
DOI = {10.2312/PE.VAMP.VAMP2013.005-009}
}