EuroVA2024

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https://diglib.eg.org/handle/10.2312/3606994

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Browsing EuroVA2024 by Subject "CCS Concepts: Human-centered computing → Visualization techniques"

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    cPro: Circular Projections Using Gradient Descent
    (The Eurographics Association, 2024) Buchmüller, Raphael; Jäckl, Bastian; Behrisch, Michael; Keim, Daniel A.; Dennig, Frederik L.; El-Assady, Mennatallah; Schulz, Hans-Jörg
    Typical projection methods such as PCA or MDS rely on mapping data onto an Euclidean space, limiting the design of resulting visualizations to lines, planes, or cubes and thus may fail to capture the intrinsic non-linear relationships within data, resulting in inefficient use of two-dimensional space. We introduce the novel projection technique -cPro-, which aligns high-dimensional data onto a circular layout. We apply gradient descent, an adaptable optimization technique to efficiently reduce a customized loss function. We use selected distance measures to reduce high data dimensionality and reveal patterns on a two-dimensional ring layout. We evaluate our approach compared to 1D and 2D MDS and discuss further use cases and potential extensions. cPro enables the design of novel visualization techniques that employ semantic distances on a circular layout.
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    Inverting Multidimensional Scaling Projections Using Data Point Multilateration
    (The Eurographics Association, 2024) Blumberg, Daniela; Wang, Yu; Telea, Alexandru; Keim, Daniel A.; Dennig, Frederik L.; El-Assady, Mennatallah; Schulz, Hans-Jörg
    Current inverse projection methods are often complex, hard to predict, and may require extensive parametrization. We present a new technique to compute inverse projections of Multidimensional Scaling (MDS) projections with minimal parametrization. We use mutilateration, a method used for geopositioning, to find data values for unknown 2D points, i.e., locations where no data point is projected. Being based on a geometrical relationship, our technique is more interpretable than comparable machine learning-based approaches and can invert 2-dimensional projections up to |D|−1 dimensional spaces given a minimum of |D| data points. We qualitatively and quantitatively compare our technique with existing inverse projection techniques on synthetic and real-world datasets using mean-squared errors (MSEs) and gradient maps. When MDS captures data distances well, our technique shows performance similar to existing approaches. While our method may show higher MSEs when inverting projected data samples, it produces smoother gradient maps, indicating higher predictability when inverting unseen points.