ShaRP provides explicit control over visual signatures of scatterplots
The method scales well with dimensionality and dataset size
ShaRP can handle any quantitative dataset with user-controllable quality trade-offs
The technique offers better adaptation to interactive visualization scenarios
📖 Full Retelling
Researchers Alister Machado, Alexandru Telea, and Michael Behrisch introduced ShaRP, a novel shape-regularized multidimensional projection technique for visualizing high-dimensional data in their paper submitted to the EuroVA Workshop 2023 on June 1, 2023, addressing the need for more user control over visual representations of complex datasets. ShaRP represents a significant advancement in dimensionality reduction methods, which are essential tools for visual exploration of high-dimensional data. Unlike existing techniques that have implicit visual signatures resulting from their algorithm design, ShaRP provides users with explicit control over how points are arranged in scatterplots, allowing for better adaptation to specific visualization scenarios and user preferences. The technique demonstrates several practical advantages: it scales well with increasing dimensionality and dataset size, can handle any quantitative dataset, and provides extended functionality for controlling projection shapes while maintaining quality metrics at a user-controllable cost.
🏷️ Themes
Data Visualization, Dimensionality Reduction, Human-Computer Interaction
Set of techniques for creating images, diagrams, or animations to communicate a message
Visualization (or visualisation in Commonwealth English; see spelling differences), also known as graphics visualization, is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstrac...
Process of reducing the number of random variables under consideration
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dim...
Data and information visualization (data viz/vis or info viz/vis) is the practice of designing and creating graphic or visual representations of quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. These visualizations are intended to help a...
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Original Source
--> Computer Science > Human-Computer Interaction arXiv:2306.00554 [Submitted on 1 Jun 2023] Title: ShaRP: Shape-Regularized Multidimensional Projections Authors: Alister Machado , Alexandru Telea , Michael Behrisch View a PDF of the paper titled ShaRP: Shape-Regularized Multidimensional Projections, by Alister Machado and Alexandru Telea and Michael Behrisch View PDF Abstract: Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics. Comments: To appear in EuroVA Workshop 2023 Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2306.00554 [cs.HC] (or arXiv:2306.00554v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2306.00554 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.2312/eurova.20231088 Focus to learn more DOI linking to related resources Submission history From: Alister Machado Dos Reis [ view email ] [v1] Thu, 1 Jun 2023 11:16:58 UTC (7,976 KB) Full-text links: Access Paper: View a PDF of the paper titled ShaRP: Shape-Regularized Multidimensional Projections, by Alister Machado and Alexandru Tel...
