Unsupervised and semi-supervised extraction of clusters from hypergraphs

Weiwei Du, Kohei Inoue, Kiichi Urahama

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data.

Original languageEnglish
Pages (from-to)2315-2318
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE89-D
Issue number7
DOIs
Publication statusPublished - Jan 1 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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