Machine Learning Methods in Visualisation for Big Data 2025

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607194
MLVis 2025 colocated with EuroVis 2025 - 27th EG Conference on Visualization
Luxembourg City, Luxembourg | June 2 - 6, 2025
Table of Contents
Papers
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
[full paper Image] [meta data Image]
Lukas Heine, Fabian Hörst, Jana Fragemann, Gijs Luijten, Jan Egger, Fin Hendrik Bahnsen, M. Saquib Sarfraz, Jens Kleesiek, and Constantin Seibold
Neighbour Embeddings: Beyond Visualisation
[full paper Image] [meta data Image]
Pierre Lambert, Edouard Couplet, Michel Verleysen, and John A. Lee

BibTeX (Machine Learning Methods in Visualisation for Big Data 2025)
@inproceedings{
10.2312:mlvis.20252013,

booktitle = {
Machine Learning Methods in Visualisation for Big Data},

editor = {
Archambault, Daniel
 and 
Nabney, Ian
 and 
Peltonen, Jaakko
},
title = {{
MLVis 2025: Frontmatter}},

author = {
Archambault, Daniel
 and 
Nabney, Ian
 and 
Peltonen, Jaakko
},
year = {
2025},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-288-2},

DOI = {
10.2312/mlvis.20252013}

}
@inproceedings{
10.2312:mlvis.20251155,

booktitle = {
Machine Learning Methods in Visualisation for Big Data},

editor = {
Archambault, Daniel
 and 
Nabney, Ian
 and 
Peltonen, Jaakko
},
title = {{
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data}},

author = {
Heine, Lukas
 and 
Hörst, Fabian
 and 
Fragemann, Jana
 and 
Luijten, Gijs
 and 
Egger, Jan
 and 
Bahnsen, Fin Hendrik
 and 
Sarfraz, M. Saquib
 and 
Kleesiek, Jens
 and 
Seibold, Constantin
},
year = {
2025},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-288-2},

DOI = {
10.2312/mlvis.20251155}

}
@inproceedings{
10.2312:mlvis.20251156,

booktitle = {
Machine Learning Methods in Visualisation for Big Data},

editor = {
Archambault, Daniel
 and 
Nabney, Ian
 and 
Peltonen, Jaakko
},
title = {{
Neighbour Embeddings: Beyond Visualisation}},

author = {
Lambert, Pierre
 and 
Couplet, Edouard
 and 
Verleysen, Michel
 and 
Lee, John Aldo
},
year = {
2025},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-288-2},

DOI = {
10.2312/mlvis.20251156}

}

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Recent Submissions

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    MLVis 2025: Frontmatter
    (The Eurographics Association, 2025) Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
  • Thumbnail Image
    Item
    Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
    (The Eurographics Association, 2025) Heine, Lukas; Hörst, Fabian; Fragemann, Jana; Luijten, Gijs; Egger, Jan; Bahnsen, Fin Hendrik; Sarfraz, M. Saquib; Kleesiek, Jens; Seibold, Constantin; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering patterns within large-scale corpora and understanding their semantic impact is critical, but depends on domain experts or resource-intensive manual reviews. In response, we introduce Spacewalker, an interactive tool designed to analyze, explore, and annotate data across multiple modalities. It allows users to extract data representations, visualize them in low-dimensional spaces and traverse large datasets either exploratorily or by querying regions of interest. We evaluated Spacewalker through extensive studies, assessing its efficacy in improving data integrity verification and annotation. We show that Spacewalker reduces time and effort compared to traditional methods. The code of this work is publicly available on https://github.com/TIO-IKIM/Spacewalker.
  • Thumbnail Image
    Item
    Neighbour Embeddings: Beyond Visualisation
    (The Eurographics Association, 2025) Lambert, Pierre; Couplet, Edouard; Verleysen, Michel; Lee, John Aldo; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    Machine learning (ML) has brought powerful tools to the visualisation community, particularly through neighbour embeddings (NE). This family of algorithms enables the intuitive visualisation of high dimensional datasets, by representing these in 2- or 3-dimensional spaces. This paper argues that as NE algorithms have progressed and diversified within the visualisation domain, they have matured into powerful yet often simple methods whose potential remains largely underutilised in broader machine learning contexts. This argument is illustrated by showing through two use cases, clustering and data preprocessing before a supervised task, how NE can contribute meaningfully with little additional algorithmic effort.