Machine Learning Methods in Visualisation for Big Data 2020

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https://diglib.eg.org/handle/10.2312/2632905

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Browsing Machine Learning Methods in Visualisation for Big Data 2020 by Subject "Visual analytics"

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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.
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    Visual Analysis of the Impact of Neural Network Hyper-Parameters
    (The Eurographics Association, 2020) Jönsson, Daniel; Eilertsen, Gabriel; Shi, Hezi; Zheng, Jianmin; Ynnerman, Anders; Unger, Jonas; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    We present an analysis of the impact of hyper-parameters for an ensemble of neural networks using tailored visualization techniques to understand the complicated relationship between hyper-parameters and model performance. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations.