Challenges of Data Visualization

NIPS 2010 Workshop (December 11, 2010; Whistler BC, Canada)


We solicit submissions for oral or poster presentation at the Workshop on Challenges of Data Visualization, to be held on December 11, 2010, in Whistler BC, Canada, in conjunction with the 24th Annual Conference on Neural Information Processing Systems (NIPS 2010).

The workshop will bring together researchers and practitioners from academia and industry to discuss the latest developments in various aspects of data visualization.

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.

As a consequence, a number of challenges arise in the context of data visualization which cannot be solved by classical methods in the field:

  • Methods have to deal with modern data formats and data sets: How can the technologies be adapted to deal with streaming and probably non i.i.d. data sets? How can specific data formats be visualized appropriately such as spatio-temporal data, spectral data, data characterized by a general probably non-metric dissimilarity measure, etc.? How can we deal with heterogeneous data and different credibility? How can the dissimilarity measure be adapted to emphasize the aspects which are relevant for visualization?

  • Available techniques for specific tasks should be combined in a canonic way: How can unsupervised learning techniques be combined to construct good visualizations? For instance, how can we effectively combine techniques for clustering, collaborative filtering, and topic modeling with dimensionality reduction to construct scatter plots that reveal the similarity between groups of data, movies, or documents? How can we arrive at context dependent visualization?

  • Visualization techniques should be accompanied by theoretical guarantees: What are reasonable mathematical specifications of data visualization to shape this inherently ill-posed problem? Can this be controlled by the user in an efficient way? How can visualization be evaluated? What are reasonable benchmarks? What are reasonable evaluation measures?

  • Visualization techniques should be ready to use for users outside the field: Which methods are suited to users outside the field? How can the necessity be avoided to set specific technical parameters by hand or choose from different possible mathematical algorithms by hand? Can this necessity be substituted by intuitive interactive mechanisms which can be used by non-experts?

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. The workshop will consist of invited tutorial talks, presentations of new research in a poster session, and panel discussions to identify the current state-of-the-art and future perspectives. Registration will be open to all NIPS 2010 Workshop attendees.

Invited Speakers


For the poster session, we encourage submissions connected to the following non-exhaustive list of topics:

  • Visualization methods for streaming data sets

  • Visualization of structures and heterogeneous objects

  • Visualization of multiple modalities and non-metric data

  • Back-projection methods

  • Parameterless models for data visualization

  • Evaluation measures of data visualization

  • Innovative combination of different machine learning tools for data visualization

  • Novel benchmarks for data visualization