IEICE TRANSACTIONS on Information
Unsupervised and Semi-Supervised Extraction of Clusters from Hypergraphs
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2315-2318
- Publication Date
- 2006/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.7.2315
- Type of Manuscript
- LETTER
- Category
- Biological Engineering
Authors
Keyword
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Weiwei DU, Kohei INOUE, Kiichi URAHAMA, "Unsupervised and Semi-Supervised Extraction of Clusters from Hypergraphs" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 7, pp. 2315-2318, July 2006, doi: 10.1093/ietisy/e89-d.7.2315.
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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2315/_p
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@ARTICLE{e89-d_7_2315,
author={Weiwei DU, Kohei INOUE, Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Unsupervised and Semi-Supervised Extraction of Clusters from Hypergraphs},
year={2006},
volume={E89-D},
number={7},
pages={2315-2318},
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.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2315},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Unsupervised and Semi-Supervised Extraction of Clusters from Hypergraphs
T2 - IEICE TRANSACTIONS on Information
SP - 2315
EP - 2318
AU - Weiwei DU
AU - Kohei INOUE
AU - Kiichi URAHAMA
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2315
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - 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.
ER -
English
