Concurrent time-series selections using deep learning and dimension reduction

TY - JOUR

T1 - Concurrent time-series selections using deep learning and dimension reduction

AU - Ali, Mohammed

AU - Borgo, Rita

AU - Jones, Mark W.

N1 - Funding Information: This work was supported by EPSRC, UK, grant number EP/N028139/1 . Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12/5

Y1 - 2021/12/5

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Dimension reduction

KW - Time-series data

KW - User interaction

KW - User study

UR - http://www.scopus.com/inward/record.url?scp=85116132087&partnerID=8YFLogxK

U2 - 10.1016/j.knosys.2021.107507

DO - 10.1016/j.knosys.2021.107507

M3 - Article

AN - SCOPUS:85116132087

SN - 0950-7051

VL - 233

JO - Knowledge-Based systems

JF - Knowledge-Based systems

M1 - 107507

ER -