Uncovering temporal patterns in visualizations of high-dimensional data

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

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.

• Jezik: Angleški jezik
• Ključne besede: temporal-data visualization, dimensionality reduction, data visualization
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 27 str.
• Številčenje: Vol. 114, iss. 2, art. 35
• PID: 20.500.12556/RUL-166905
• UDK: 004
• ISSN pri članku: 0885-6125
• DOI: 10.1007/s10994-025-06734-z
• COBISS.SI-ID: 224462339
• Datum objave v RUL: 30.01.2025

Gradivo je del revije

• Naslov: Machine learning
• Skrajšan naslov: Mach. learn.
• Založnik: Springer Nature
• ISSN: 0885-6125
• COBISS.SI-ID: 2623527

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: vizualizacija časovnih podatkov, zmanjševanje dimenzij, vizualizacija podatkov

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0209
• Naslov: Umetna inteligenca in inteligentni sistemi

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: V2-2272
• Naslov: Opredelitev okvira za zagotavljanje zaupanja javnosti v sisteme umetne inteligence in njihove uporabe