Journal
KNOWLEDGE-BASED SYSTEMS
Volume 233, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2021.107507
Keywords
User interaction; User study; Dimension reduction; Time-series data; Deep Learning
Categories
Funding
- EPSRC [EP/N028139/1] Funding Source: UKRI
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The study aimed to investigate the relationship between 1D time-series data and 2D representation provided by dimension reduction techniques from a user perspective. Results showed that linking these views can positively impact speed and accuracy.
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. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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