DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

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dc.contributor.authorSALMANIAN, PARISAen_US
dc.contributor.authorChatzimparmpas, Angelosen_US
dc.contributor.authorKaraca, Ali Canen_US
dc.contributor.authorMartins, Rafael M.en_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorNabney, Ianen_US
dc.contributor.editorPeltonen, Jaakkoen_US
dc.date.accessioned2024-05-21T08:51:16Z
dc.date.available2024-05-21T08:51:16Z
dc.date.issued2024
dc.description.abstractDimensionality 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.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20241125
dc.identifier.isbn978-3-03868-256-1
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/mlvis.20241125
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/mlvis20241125
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing→Visualization; Visual analytics; Machine learning→Unsupervised learning
dc.subjectHuman centered computing→Visualization
dc.subjectVisual analytics
dc.subjectMachine learning→Unsupervised learning
dc.titleDimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machineen_US
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Machine Learning Methods in Visualisation for Big Data 2024