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Uncovering temporal patterns in visualizations of high-dimensional data
ID
Poličar, Pavlin Gregor
(
Author
),
ID
Zupan, Blaž
(
Author
)
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https://link.springer.com/article/10.1007/s10994-025-06734-z
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Abstract
With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic data analysis.
Language:
English
Keywords:
temporal-data visualization
,
dimensionality reduction
,
data visualization
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FRI - Faculty of Computer and Information Science
Publication status:
Published
Publication version:
Version of Record
Year:
2025
Number of pages:
27 str.
Numbering:
Vol. 114, iss. 2, art. 35
PID:
20.500.12556/RUL-166905
UDC:
004
ISSN on article:
0885-6125
DOI:
10.1007/s10994-025-06734-z
COBISS.SI-ID:
224462339
Publication date in RUL:
30.01.2025
Views:
119
Downloads:
805
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Record is a part of a journal
Title:
Machine learning
Shortened title:
Mach. learn.
Publisher:
Springer Nature
ISSN:
0885-6125
COBISS.SI-ID:
2623527
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Secondary language
Language:
Slovenian
Keywords:
vizualizacija časovnih podatkov
,
zmanjševanje dimenzij
,
vizualizacija podatkov
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0209
Name:
Umetna inteligenca in inteligentni sistemi
Funder:
ARRS - Slovenian Research Agency
Project number:
V2-2272
Name:
Opredelitev okvira za zagotavljanje zaupanja javnosti v sisteme umetne inteligence in njihove uporabe
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