A Mixed-Initiative Interface for Interactive Distance Metric Learning
We present MindMiner, a mixed-initiative interface for capturing subjective similarity measurements via a combination of new interaction techniques and machine learning algorithms. MindMiner collects qualitative, hard to express similarity measurements from users via active polling with uncertainty and example based visual constraint creation. MindMiner also formulates human prior knowledge into a set of inequalities and learns a quantitative similarity distance metric via convex optimization. In a 12-subject peer-review understanding task, we found MindMiner was easy to learn and use, and could capture users’ implicit knowledge about writing performance and cluster target entities into groups that match subjects’ mental models. We also found that MindMiner’s constraint suggestions and uncertainty polling functions could improve both efficiency and the quality of clustering.
- Fan, X., Liu, Y., Cao, N., Hong, J., Wang, J., MindMiner: A Mixed-Initiative Interface for Interactive Distance Metric Learning, Proceedings of International Conference on Human-Computer Interaction (INTERACT 2015), Bamberg, Germany, September 14 – 18, 2015. ( pdf )
- Fan, X., Liu, Y., Cao, N., Hong, J., Wang, J., MindMiner: Quantifying Entity Similarity via Interactive Distance Metric Learning, Demo, 20th ACM Conference on Intelligent User Interfaces (IUI 2015), Atalanta, GA, March 29 – April 1, 2015. ( pdf )