University of Illinois Urbana-Champaign

A Unified Framework to Integrate Supervision and Metric Learning into Clustering

Li, Xin; Roth, Dan

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A Unified Framework to Integrate Supervision and Metric Learning into Clustering.pdf

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https://hdl.handle.net/2142/10922

Description

Title
A Unified Framework to Integrate Supervision and Metric Learning into Clustering
Author(s)
  • Li, Xin
  • Roth, Dan
Issue Date
2004-12
Keyword(s)
Artificial Intelligence Machine Learning
Date of Ingest
2009-04-17T15:03:37Z
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.
Type of Resource
text
Genre of Resource
Technical Report
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http://hdl.handle.net/2142/10922
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