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Concurrent time-series selections using deep learning and dimension reduction

Mohammed Ali, Rita Borgo, Mark Jones Orcid Logo

Knowledge-Based Systems, Volume: 233, Start page: 107507

Swansea University Author: Mark Jones Orcid Logo

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Abstract

The objective of this work was to investigate from a user perspective linkage between a 1D time-series view of data and a 2D representation provided by dimension reduction techniques. Our hypothesis is that when such interaction happens seamlessly, the use of these linked views, compared to only int...

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Published in: Knowledge-Based Systems
ISSN: 0950-7051
Published: Elsevier BV 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57939
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Abstract: The objective of this work was to investigate from a user perspective linkage between a 1D time-series view of data and a 2D representation provided by dimension reduction techniques. Our hypothesis is that when such interaction happens seamlessly, the use of these linked views, compared to only interacting with the 1D time-series view, for the ubiquitous task of selection and labelling, is more efficient and effective both in terms of performance and user experience. To this end we examine different dimension reduction techniques (UMAP, t-SNE, PCA and Autoencoder) and evaluate each technique within our experimental setting. Results demonstrate that there is a positive impact on speed and accuracy through augmenting 1D views with a dimension reduction 2D view when these views are linked and linkage is supported through coordinated interaction.
Keywords: User interaction; User study; Dimension reduction; Time-series data; Deep Learning
College: Faculty of Science and Engineering
Funders: EPSRC
Start Page: 107507