In order to handle big data challenges, machine learning techniques can be advantageous in simplifying and summarising large data sets for visualisation. Machine learning provides methods that allow the summarisation of very large data sets whereas visualisation leverages the human visual system to help find unanticipated patterns. In this tutorial, we cover machine learning methods relevant to the area of visualisation. In addition to an exploration of the applicability, strengths, and weaknesses of such approaches, we provide links to available software tools that can help provide solutions to machine learning problems.

Main Topics of the Tutorial

1. Supervised methods (regression and classification) and supervised dimensionality reduction
2. Methods for text data
3. Methods for dynamic data
4. HCI for dimensionality reduction
5. Data lab: bring your own data to be analysed with help from the presenters

• 14:15-14:20 Introduction and motivation (5mins) [Presenters: Daniel Archambault and Ian Nabney, slides]
• 14:20-14:40 Supervised dimensionality reduction, part 1 (20mins) [Presenter: Jaakko Peltonen, slides]
  • Supervised dimensionality reduction by methods that optimize supervised criteria
  • Supervised dimensionality reduction by linear metric learning
  • Supervised dimensionality reduction by nonlinear metric learning
• 14:40-15:00 Supervised dimensionality reduction, part 2 (20mins) [Presenter: Ian Nabney, slides]
  • Semi-supervised tasks; weighted combinations of cost functions
  • Models for semi-supervised visualisation: variants of Generative Topographic Mapping and Neuroscale
  • Case studies
• 15:00-15:20 Methods for text data, part 1 (20mins) [Presenter: Daniel Archambault, slides]
  • Methods for visualising hierarchically clustered microblogging
  • Sentiment visualisation for hierarchically clustered microblogging
  • Visualisation of blog data
  • Relevant state-of-the-art survey
• 15:20-15:40 Methods for text data, part 2 (20mins) [Presenter: Jaakko Peltonen, slides]
  • Dimensionality reduction of text data by topic modeling
  • Nonparametric topic models
• 15:40-15:55 Methods for dynamic data, part 1 (15mins) [Presenter: Ian Nabney, slides]
• 15:55-16:30 Coffee Break
• 16:30-16:45 Methods for dynamic data, part 2 (15mins) [Presenter: Ian Nabney]
  • Nature of time series data
  • Capturing temporal dependencies: GTM through time
  • Case study: condition monitoring of helicopter airframes
• 16:45-17:15 Methods for dynamic data, part 3 (30mins) [Presenter: Daniel Archambault, slides]
  • Effectiveness of methods in visualising dynamic graphs
  • Effectiveness of the mental map (drawing stability)
  • Lessons learned from the series of experiments
• 17:15-17:25 HCI in visualisation, part 1 (10mins) [Presenter: Daniel Archambault, slides]
  • An experiment to compare human and automated community finding
• 17:25-17:35 HCI in visualisation, part 2 (10mins) [Presenter: Ian Nabney, slides]
  • Controlled experiment to compare immersive and 'externalised' visualisation
• 17:35-18:05 Data lab [Presenter: all]
• 18:05-18:10 Closing of the tutorial

Ian Nabney is 50th Anniversary Chair of Systems Analytics and the Executive Dean of the School of Engineering and Applied Science at Aston University. He received his BA in Mathematics from Oxford University and a PhD in Mathematics from Cambridge University. He has over 20 years’ experience in machine learning research, has published more than 80 papers (1900 citations), and is the system architect for the Netlab pattern analysis toolbox, which has been downloaded more than 40,000 times since 1999 (the accompanying book has been through three reprints), and the Data Visualisation and Modelling System (DVMS) which integrates data projection and information visualisation techniques to provide a rich interactive environment for data exploration and visual analytics. DVMS will be used for the demonstrations of generative models. He has won grants worth more than 3M GBP from EPSRC, the EU, TSB, and industry and has supervised 11 PhD students to completion. He is the Chair of the Natural Computing Applications Forum, a principal mechanism in the UK for exchange of ideas between academics and industry on natural computing technology and practical applications.

Jaakko Peltonen is an associate professor of statistics (data analysis) at the School of Information Sciences, University of Tampere where he leads the Statistical Machine Learning and Exploratory Data Analysis research group; he is also currently visiting associate professor at Aalto University where he is a PI of the Probabilistic Machine Learning research group. He received his D.Sc. from Helsinki University of Technology in 2004. He is an associate editor of Neural Processing Letters and an editorial board member of Heliyon. He has served in organising committees of seven international conferences and one international summer school and in program committees of 24 international conferences/workshops, and has referee duties for numerous international journals and conferences. He is an expert in statistical machine learning methods for exploratory data analysis, visualisation of data, and learning from multiple sources.

Daniel Archambault received his PhD in Computer Science from the University of British Columbia, Canada in 2008. He is currently a Senior Lecturer of Computer Science at Swansea University in the United Kingdom. During his post-doctoral studies at University College Dublin, he applied his expertise in information visualisation to help visualise the results of machine learning approaches, particularly in the area of social media visualisation. This work inspired him to co-chair the AAAI ICWSM Workshop on Social Media Visualisation (SocMedVis 2012 and 2013). His other areas expertise primarily lie in graph visualisation and drawing as well as perceptual factors in information visualisation.