DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncovering general dataset patterns, these methods may introduce artifacts and suffer from interpretability issues. This paper presents DimVis, a visualization tool that employs supervised Explainable Boosting Machine (EBM) models (trained on user-selected data of interest) as an interpretation assistant for DR projections. Our tool facilitates high-dimensional data analysis by providing an interpretation of feature relevance in visual clusters through interactive exploration of UMAP projections. Specifically, DimVis uses a contrastive EBM model that is trained in real time to differentiate between the data inside and outside a cluster of interest. Taking advantage of the inherent explainable nature of the EBM, we then use this model to interpret the cluster itself via single and pairwise feature comparisons in a ranking based on the EBM model's feature importance. The applicability and effectiveness of DimVis are demonstrated via a use case and a usage scenario with real-world data. We also discuss the limitations and potential directions for future research.
Description
CCS Concepts: Human-centered computing→Visualization; Visual analytics; Machine learning→Unsupervised learning
@inproceedings{10.2312:mlvis.20241125,
booktitle = {Machine Learning Methods in Visualisation for Big Data},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
title = {{DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine}},
author = {SALMANIAN, PARISA and Chatzimparmpas, Angelos and Karaca, Ali Can and Martins, Rafael M.},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-256-1},
DOI = {10.2312/mlvis.20241125}
}