Title: Interactive Model Learning from High-Dimensional Data: A Visual Analytics Approach
Speaker: Klaus Mueller, Stony Brook University
Date: Monday, December 6, 2010 3:00 - 4:00 pm***
Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ
**Please note time change
The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight from the raw block of data and finally create a formal model capturing this insight. This process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. High-quality visual feedback can play a decisive role here. To this end, I will present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more interactive and efficient. In addition, I will also discuss various platforms we have developed over the years to make the exploration of multivariate (high-dimensional) data more intuitive and direct. Here I will discuss our recent work on illustrative parallel coordinates, space embedding, and multivariate scatterplots.
Slides: Interactive Model Learning from High-Dimensional Data: A Visual Analytics Approach