Browsing by Author "Comba, João L. D."

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    Guided Stable Dynamic Projections
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Vernier, Eduardo Faccin; Comba, João L. D.; Telea, Alexandru C.; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Projections aim to convey the relationships and similarity of high-dimensional data in a low-dimensional representation. Most such techniques are designed for static data. When used for time-dependent data, they usually fail to create a stable and suitable low dimensional representation. We propose two dynamic projection methods (PCD-tSNE and LD-tSNE) that use global guides to steer projection points. This avoids unstable movement that does not encode data dynamics while keeping t-SNE's neighborhood preservation ability. PCD-tSNE scores a good balance between stability, neighborhood preservation, and distance preservation, while LD-tSNE allows creating stable and customizable projections. We compare our methods to 11 other techniques using quality metrics and datasets provided by a recent benchmark for dynamic projections.
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    Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Vernier, Eduardo Faccin; Garcia, Rafael; Silva, Iron Prando da; Comba, João L. D.; Telea, Alexandru C.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.