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Title:A Unified Framework to Integrate Supervision and Metric Learning into Clustering
Author(s):Li, Xin; Roth, Dan
Subject(s):Artificial Intelligence Machine Learning
Abstract:In this paper, we propose a unified framework for applying supervision to discriminative clustering, which: (1) explicitly formalizes the problem of training a partition function as a supervised metric-learning process; (2) learns a partition function that can optimize a supervised clustering error; and (3) flexibly learns a distance metric with regard to any clustering algorithm. Moreover, we develop a general gradient-descent learning algorithm that trains a distance metric under this framework. The convergence of this algorithm is guaranteed for some restricted cases. Our experimental study shows the significant performance improvement after integrating supervision and distance metric learning in clustering, trained in our new framework.
Issue Date:2004-12
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2004-2488
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-17

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