Progressive Multidimensional Projections: A Process Model based on Vector Quantization

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dc.contributor.authorVentocilla, Elio Alejandroen_US
dc.contributor.authorMartins, Rafael M.en_US
dc.contributor.authorPaulovich, Fernando V.en_US
dc.contributor.authorRiveiro, Mariaen_US
dc.contributor.editorArchambault, Daniel and Nabney, Ian and Peltonen, Jaakkoen_US
dc.date.accessioned2020-05-24T13:27:41Z
dc.date.available2020-05-24T13:27:41Z
dc.date.issued2020
dc.description.abstractAs large datasets become more common, so becomes the necessity for exploratory approaches that allow iterative, trial-anderror analysis. Without such solutions, hypothesis testing and exploratory data analysis may become cumbersome due to long waiting times for feedback from computationally-intensive algorithms. This work presents a process model for progressive multidimensional projections (P-MDPs) that enables early feedback and user involvement in the process, complementing previous work by providing a lower level of abstraction and describing the specific elements that can be used to provide early system feedback, and those which can be enabled for user interaction. Additionally, we outline a set of design constraints that must be taken into account to ensure the usability of a solution regarding feedback time, visual cluttering, and the interactivity of the view. To address these constraints, we propose the use of incremental vector quantization (iVQ) as a core step within the process. To illustrate the feasibility of the model, and the usefulness of the proposed iVQ-based solution, we present a prototype that demonstrates how the different usability constraints can be accounted for, regardless of the size of a dataset.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20201099
dc.identifier.isbn978-3-03868-113-7
dc.identifier.pages1-5
dc.identifier.urihttps://doi.org/10.2312/mlvis.20201099
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20201099
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.titleProgressive Multidimensional Projections: A Process Model based on Vector Quantizationen_US
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Machine Learning Methods in Visualisation for Big Data 2020