01.03.15 - 06.03.15, Seminar 15101

Bridging Information Visualization with Machine Learning

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

This Dagstuhl Seminar aims at bringing the visualization and machine learning communities together.

Information visualization and visual data mining leverage the human visual system to provide insight and understanding of unorganized data. Visualizing data in a way that is appropriate for the user's needs proves essential in a number of situations: getting insights about data before a further more quantitative analysis (e.g., for expert selection of a number of clusters in a data set), presenting data to a user through well-chosen table, graph or other structured representations, relying on the cognitive skills of humans to show them extended information in a compact way, etc.

The scalability of visualization methods is an issue: Human vision is intrinsically limited to between two and three dimensions, and the human preattentive system cannot handle more than a few combined features. In addition the computational burden of many visualization methods is too large for real time interactive use with large datasets. In order to address these scalability issues and to enable visual data mining of massive sets of high dimensional data (or so-called ''big data''), simplification methods are needed, so as to select and/or summarize important dimensions and/or objects.

Traditionally, two scientific communities developed tools to address these problems: the machine learning (ML) and information visualization (IV) communities. On the one hand, ML provides a collection of automated data summarizing/compression solutions. Clustering algorithms summarize a set of objects with a smaller set of prototypes, while projection algorithms reduce the dimensionality of objects described by high-dimensional vectors. On the other hand, the IV community has developed user-centric and interactive methods to handle the human vision scalability issue.

This Dagstuhl Seminar follows Seminar 12081, which provided to the participants from the IV and ML communities the ground for understanding each other. This new seminar will build on these grounds, and address key challenges such as interactivity, quality assessment, platforms and software, and others. The seminar will be organized in an interactive way. It is intended to start discussions by short presentations focused on open questions. As far as possible, these opening presentations should be made by pairs of researchers coming from the ML and IV fields, and prepared before the seminar. Most of the time slots will then be devoted to discussions. Structuring short talks will be inserted between discussions, and prepared on the fly by ''discussion leaders''. Key goals at short and long terms for work to be carried out by joining forces from the ML and IV fields will be identified as a conclusion of the seminar.