Workshop
Challenges of Data Visualization
Barbara Hammer · Laurens van der Maaten · Fei Sha · Alexander Smola
Sat 11 Dec, 7:30 a.m. PST
The increasing amount and complexity of electronic data sets turns visualization into a key technology to provide an intuitive interface to the information. Unsupervised learning has developed powerful techniques for, e.g., manifold learning, dimensionality reduction, collaborative filtering, and topic modeling. However, the field has so far not fully appreciated the problems that data analysts seeking to apply unsupervised learning to information visualization are facing such as heterogeneous and context dependent objectives or
streaming and distributed data with different credibility. Moreover, the unsupervised learning field has hitherto failed to develop human-in-the-loop approaches to data visualization, even though such approaches including e.g. user relevance feedback are necessary to arrive at valid and interesting results.\par As a consequence, a number of challenges arise in the context of data visualization which cannot be solved by classical methods in the field:
The goal of the workshop is to identify the state-of-the-art with respect to these challenges and to discuss possibilities to meet these demands with modern techniques.
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