Uncovering temporal patterns in visualizations of high-dimensional data

Poličar, Pavlin Gregor (author), Zupan, Blaž (author)

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

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