A Unified Framework to Integrate Supervision and Metric Learning into Clustering

Li, Xin; Roth, Dan

Permalink

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

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

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

Permalink

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

Copyright and License 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).

Owning Collections