Concurrent time-series selections using deep learning and dimension reduction

被引:14
作者
Ali, Mohammed [1 ,2 ]
Borgo, Rita [3 ]
Jones, Mark W. [1 ]
机构
[1] Swansea Univ, Swansea, W Glam, Wales
[2] King Khalid Univ, Abha, Saudi Arabia
[3] Kings Coll London, London, England
基金
英国工程与自然科学研究理事会;
关键词
User interaction; User study; Dimension reduction; Time-series data; Deep Learning; EXPLORATORY ANALYSIS; VISUAL ANALYTICS; CLASSIFICATION; VISUALIZATION; SYSTEM;
D O I
10.1016/j.knosys.2021.107507
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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/).
引用
收藏
页数:16
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