Work

Concurrent time-series selections using deep learning and dimension reduction

Year: 2021

Type: article

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.

Source: Knowledge-Based Systems

DOI: 10.1016/j.knosys.2021.107507

Authors: Mohammed Ali, Rita Borgo, Mark W. Jones

Institutions: Swansea University, King Khalid University, King's College London

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,850

Funder: Engineering and Physical Sciences Research Council