Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data
Summary :
A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.9 pp.1456-1459
- Publication Date
- 2007/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.9.1456
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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Weiwei DU, Kiichi URAHAMA, "Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 9, pp. 1456-1459, September 2007, doi: 10.1093/ietisy/e90-d.9.1456.
Abstract: A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.9.1456/_p
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@ARTICLE{e90-d_9_1456,
author={Weiwei DU, Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data},
year={2007},
volume={E90-D},
number={9},
pages={1456-1459},
abstract={A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.},
keywords={},
doi={10.1093/ietisy/e90-d.9.1456},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data
T2 - IEICE TRANSACTIONS on Information
SP - 1456
EP - 1459
AU - Weiwei DU
AU - Kiichi URAHAMA
PY - 2007
DO - 10.1093/ietisy/e90-d.9.1456
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2007
AB - A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.
ER -
English
