Semi-supervised clustering with pairwise constraints: A discriminative approach

Zhengdong Lu, Todd K. Leen

Research output: Contribution to journalConference article

16 Scopus citations

Abstract

We consider the semi-supervised clustering problem where we know (with varying degree of certainty) that some sample pairs are (or are not) in the same class. Unlike previous efforts in adapting clustering algorithms to incorporate those pairwise relations, our work is based on a discriminative model. We generalize the standard Gaussian process classifier (GPC) to express our classification preference. To use the samples not involved in pairwise relations, we employ the graph kernels (covariance matrix) based on the entire data set. Experiments on a variety of data sets show that our algorithm significantly outperforms several state-of-the-art methods.

Original languageEnglish (US)
Pages (from-to)299-306
Number of pages8
JournalJournal of Machine Learning Research
Volume2
StatePublished - Dec 1 2007
Event11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 - San Juan, Puerto Rico
Duration: Mar 21 2007Mar 24 2007

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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