Sign up & Download
Sign in

Learning a Mahalanobis distance metric for data clustering and classification

by S Xiang, F Nie, C Zhang
Pattern Recognition ()

Abstract

Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm.

Cite this document (BETA)

Readership Statistics

14 Readers on Mendeley
by Discipline
 
 
 
by Academic Status
 
57% Ph.D. Student
 
14% Associate Professor
 
7% Lecturer
by Country
 
14% United States
 
7% Portugal

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Already have an account? Sign in