Next Article in Journal
A Systematic Review of Adaptivity in Human-Robot Interaction
Next Article in Special Issue
Sense-making Strategies for the Interpretation of Visualizations—Bridging the Gap between Theory and Empirical Research
Previous Article in Journal
Form, Function and Etiquette–Potential Users’ Perspectives on Social Domestic Robots
Article Menu

Export Article

Open AccessArticle
Multimodal Technologies Interact. 2017, 1(3), 13; doi:10.3390/mti1030013

Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation

1
Department of Computer Science, Middlesex University, London NW4 4BT, UK
2
Department of Electrical, Electronic, Computers and Systems Engineering, University of Oviedo, Oviedo 33002, Spain
3
Department of Computer Science, University of London, London EC1V 0HB, UK
4
CGI Defence Innovation, Science & Technology, CGI IT UK Limited, London N1 9AG, UK
5
Aberdeen Business School, Robert Gordon University, Aberdeen AB10 7QE, UK
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 8 July 2017
(This article belongs to the Special Issue Coupling Computation and Human Cognition through Interaction Design)
View Full-Text   |   Download PDF [458 KB, uploaded 11 July 2017]   |  

Abstract

There has been extensive research on dimensionality reduction techniques. While these make it possible to present visually the high-dimensional data in 2D or 3D, it remains a challenge for users to make sense of such projected data. Recently, interactive techniques, such as Feature Transformation, have been introduced to address this. This paper describes a user study that was designed to understand how the feature transformation techniques affect user’s understanding of multi-dimensional data visualisation. It was compared with the traditional dimension reduction techniques, both unsupervised (PCA) and supervised (MCML). Thirty-one participants were recruited to detect visual clusters and outliers using visualisations produced by these techniques. Six different datasets with a range of dimensionality and data size were used in the experiment. Five of these are benchmark datasets, which makes it possible to compare with other studies using the same datasets. Both task accuracy and completion time were recorded for comparison. The results show that there is a strong case for the feature transformation technique. Participants performed best with the visualisations produced with high-level feature transformation, in terms of both accuracy and completion time. The improvements over other techniques are substantial, particularly in the case of the accuracy of the clustering task. However, visualising data with very high dimensionality (i.e., greater than 100 dimensions) remains a challenge. View Full-Text
Keywords: human-centered computing; empirical studies; visual analytics; dimensionality reduction human-centered computing; empirical studies; visual analytics; dimensionality reduction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xu, K.; Zhang, L.; Pérez, D.; Nguyen, P.H.; Ogilvie-Smith, A. Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation. Multimodal Technologies Interact. 2017, 1, 13.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Multimodal Technologies Interact. EISSN 2414-4088 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top