Interaction and User Integration in Machine Learning for Information Visualisation

Many methods have been developed in machine learning (ML) for information visualisation (infovis). For example, PCA, MDS, t-SNE and improvements are standard tools to reduce the dimensionality of high dimensional datasets for visualisation purposes. However, multiple other means are regularly used in the field of infovis when tackling datasets with high dimensionality. Letting the user manipulate the visualisation is one of these means, either through selection, navigation or filtering. Introducing manipulation of the visualisation also integrates the user as a core aspect of a given system. In the context of machine learning, beyond the informational and exploratory use of infovis, users' feedback can for example be highly informational to drive the dimensionality reduction process. This special session of the ESANN conference is a followup of the special session on "Information Visualisation and Machine Learning: Techniques, Validation and Integration" at ESANN 2016. It aims to gather researchers that integrate users in the core of ML methods for infovis. New algorithms and frameworks are welcome, as well as experimental use cases that bring new insight in the integration of interaction and user integration in ML for infovis. This special session aims to provide practitioners from both communities a common forum of discussion where issues at the crossroads of machine learning and information visualisation could be discussed.