Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Dimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset while preserving local and global structures. By representing a high-dimensional dataset as a twodimensional scatterplot, a user can explore structures within the dataset. However, dimensionality reductions inherit distortions that might result in false deductions. This work presents a visualization approach that combines a two-dimensional scatterplot derived from a dimensionality reduction with two pointwise filtering possibilities. Each point is associated with two pointwise metrics that quantify the correctness of its neighborhood and similarity to surrounding data points. By setting threshold for these two metrics, the user is supported in several scatterplot analytics tasks, e.g., class separation and outlier detection. We apply our visualization to a text corpus to detect interesting data points visually and discuss the findings.
Description
CCS Concepts: Human-centered computing → Information visualization; Visual analytics
@inproceedings{10.2312:cgvc.20241224,
booktitle = {Computer Graphics and Visual Computing (CGVC)},
editor = {Hunter, David and Slingsby, Aidan},
title = {{Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings}},
author = {Atzberger, Daniel and Jobst, Adrian and Scheibel, Willy and Döllner, Jürgen},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {10.2312/cgvc.20241224}
}