Interactive Learning Using Manifold Geometry

We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data points to the correct output level. Each repositioned data point acts as a control point for altering the learned model, using the geometry underlying the data. We capture the underlying structure of the data as a manifold, on which we compute a set of basis functions as the foundation for learning. Our results show that manifold-based interactive learning achieves dramatic improvement over alternative approaches.

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Eric Eaton, Gary Holness, Daniel McFarlane
• Keywords: AAAI 2010, Data Point, Intelligent Agents, Interactive Learning, Interactive Learning Method