Machine Learning Methods in Visualisation for Big Data 2020

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Browsing Machine Learning Methods in Visualisation for Big Data 2020 by Subject "Human centered computing"

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    Progressive Multidimensional Projections: A Process Model based on Vector Quantization
    (The Eurographics Association, 2020) Ventocilla, Elio Alejandro; Martins, Rafael M.; Paulovich, Fernando V.; Riveiro, Maria; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    As 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.