IEICE TRANSACTIONS on Information
Semi-Supervised Classification with Spectral Subspace Projection of Data
Summary :
A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.1 pp.374-377
- Publication Date
- 2007/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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Weiwei DU, Kiichi URAHAMA, "Semi-Supervised Classification with Spectral Subspace Projection of Data" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 1, pp. 374-377, January 2007, doi: .
Abstract: A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/e90-d_1_374/_p
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@ARTICLE{e90-d_1_374,
author={Weiwei DU, Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Semi-Supervised Classification with Spectral Subspace Projection of Data},
year={2007},
volume={E90-D},
number={1},
pages={374-377},
abstract={A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.},
keywords={},
doi={},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Semi-Supervised Classification with Spectral Subspace Projection of Data
T2 - IEICE TRANSACTIONS on Information
SP - 374
EP - 377
AU - Weiwei DU
AU - Kiichi URAHAMA
PY - 2007
DO -
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2007
AB - A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.
ER -
English
