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MS Final Examination – Sicheng Xiong


Thursday, April 25, 2013 1:00 PM - 3:00 PM

Active Learning of Constraints for Semi-Supervised Clustering
Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints.  In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semi-supervised clustering. We consider active learning in an iterative manner where in each iteration queries are selected based on the current clustering solution and the existing constraint set. We apply a general framework that builds on the concept of neighborhood, where neighborhoods contain “labeled examples” of different clusters according to the pairwise constraints. Our active learning method expands the neighborhoods by selecting informative points and querying their relationship with the neighborhoods. Under this framework, we build on the classic uncertainty-based principle and present a novel approach for computing the uncertainty associated with each data point. We further introduce a selection criterion that trades-off the amount of uncertainty of each data point with the expected number of queries (the cost) required to resolve this uncertainty. This allows us to select queries that have the highest information rate. We evaluate the proposed method on the benchmark datasets and the results demonstrate consistent and substantial improvements over the current state-of-the-art.

Major Advisor: Xiaoli Fern
Committee: Prasad Tadepalli
Committee: Weng-Keen Wong
GCR: Bernhard Jenny


Kelley Engineering Center (campus map)
1007
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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