Concurrent time-series selections using deep learning and dimension reduction
Concurrent time-series selections using deep learning and dimension reduction
复制标题
DOI：
10.1016/j.knosys.2021.107507
复制
发表时间：
2021-10-02
影响因子：
8.8
通讯作者：
Jones, Mark W.
中科院分区：
文献类型：
--
作者：
Ali, Mohammed;Borgo, Rita;Jones, Mark W.
这项工作的目的是调查从用户的角度来看，数据的一维时间序列视图和降维技术提供的二维表示之间的联系。我们的假设是，当这种交互无缝发生时，与仅与1D时间序列视图交互相比，使用这些链接视图来执行无处不在的选择和标记任务，在性能和用户方面都更高效和有效体验。为此，我们研究了不同的降维技术（UMAP，t-SNE，PCA和Autoencoder），并在我们的实验环境中评估每种技术。结果表明，有一个积极的影响的速度和准确性，通过增加一维视图与降维的二维视图时，这些视图是链接和链接是通过协调的互动支持。(c)2021年，任作者。由爱思唯尔公司出版。这是一篇开放获取的文章，使用CC BY许可证（http：//creativecommons.org/licenses/by/4.0/）。
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/).