1. Semi-supervised Learning
COMP Seminar
Spring 2011

2. Similarity Based Methods
Questions: given a set of points and the class labels, can we learn a distance matrix such that intra-cluster distance are minimized and inter-cluster distance are maximized?

3. Distance metric learning
Define a new distance measure of the form:
Linear transformation of the original data

4. Distance metric learning

5. Semi-Supervised Clustering Example Similarity Based

6. Semi-Supervised Clustering Example Distances Transformed by Learned Metric

7. Semi-Supervised Clustering Example Clustering Result with Trained Metric

8. Evaluation
Source: E. Xing, et al. Distance metric learning

9. Evaluation
Source: E. Xing, et al. Distance metric learning

10. Additional Readings
Combining Similarity and Search-Based Semi-Supervised Clustering “Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering”, Basu, et al.
Ontology based semi-supervised clustering “A framework for ontology-driven subspace clustering”, Liu et al.

11. References UT machine learning group
Semi-supervised Clustering by Seeding
Constrained K-means clustering with background knowledge
Some slides are from Jieping Ye at Arizona State