Semi-supervised Learning COMP 790-90 Seminar Spring 2011

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?

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

Distance metric learning

Semi-Supervised Clustering Example Similarity Based

Semi-Supervised Clustering Example Distances Transformed by Learned Metric

Semi-Supervised Clustering Example Clustering Result with Trained Metric

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

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

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.

References UT machine learning group http://www.cs.utexas.edu/~ml/publication/unsupervised.html Semi-supervised Clustering by Seeding http://www.cs.utexas.edu/users/ml/papers/semi-icml-02.pdf Constrained K-means clustering with background knowledge http://www.litech.org/~wkiri/Papers/wagstaff-kmeans-01.pdf Some slides are from Jieping Ye at Arizona State