AAAI Publications, Twenty-Fourth AAAI Conference on Artificial Intelligence

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Interactive Learning Using Manifold Geometry
Eric Eaton, Gary Holness, Daniel McFarlane

Last modified: 2010-07-03


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 instances to the correct output level. Each repositioned data instance 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 improves performance monotonically with each correction, outperforming alternative approaches.


manifold learning; interactive learning; spectral graph theory; Laplacian regularization; human-computer interaction

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