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[2306.00554] ShaRP: Shape-Regularized Multidimensional Projections
Summary
The paper introduces ShaRP, a novel projection technique for dimensionality reduction that allows users to control the visual signature of scatterplots, enhancing interactive data visualization.
Why It Matters
Dimensionality reduction is crucial for visualizing high-dimensional data. ShaRP's user-controlled projection shapes improve the interpretability of data visualizations, making it a valuable tool for researchers and practitioners in fields like AI and data science.
Key Takeaways
- ShaRP enables explicit control over the visual representation of data projections.
- The technique scales effectively with both dimensionality and dataset size.
- ShaRP is versatile, handling any quantitative dataset with minimal quality trade-offs.
- Improved control over projections enhances user interaction in data visualization.
- The method addresses limitations of existing dimensionality reduction techniques.
Computer Science > Human-Computer Interaction arXiv:2306.00554 (cs) [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: 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 versi...
