Explaining Neighborhood Preservation for Multidimensional Projections
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Dimensionality reduction techniques are the tools of choice for exploring high-dimensional datasets by means of low-dimensional projections. However, even state-of-the-art projection methods fail, up to various degrees, in perfectly preserving the structure of the data, expressed in terms of inter-point distances and point neighborhoods. To support better interpretation of a projection, we propose several metrics for quantifying errors related to neighborhood preservation. Next, we propose a number of visualizations that allow users to explore and explain the quality of neighborhood preservation at different scales, captured by the aforementioned error metrics.We demonstrate our exploratory views on three real-world datasets and two state-of-the-art multidimensional projection techniques.
Description
@inproceedings{10.2312:cgvc.20151234,
booktitle = {Computer Graphics and Visual Computing (CGVC)},
editor = {Rita Borgo and Cagatay Turkay},
title = {{Explaining Neighborhood Preservation for Multidimensional Projections}},
author = {Martins, Rafael Messias and Minghim, Rosane and Telea, Alexandru C.},
year = {2015},
publisher = {The Eurographics Association},
ISBN = {978-3-905674-94-1},
DOI = {10.2312/cgvc.20151234}
}