Interactive Learning Using Manifold Geometry
Abstract
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.
Cite
Text
Eaton et al. "Interactive Learning Using Manifold Geometry." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7688Markdown
[Eaton et al. "Interactive Learning Using Manifold Geometry." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/eaton2010aaai-interactive/) doi:10.1609/AAAI.V24I1.7688BibTeX
@inproceedings{eaton2010aaai-interactive,
title = {{Interactive Learning Using Manifold Geometry}},
author = {Eaton, Eric and Holness, Gary and McFarlane, Daniel},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {437-443},
doi = {10.1609/AAAI.V24I1.7688},
url = {https://mlanthology.org/aaai/2010/eaton2010aaai-interactive/}
}