Semi-supervised classification with spectral subspace projection of data

Weiwei Du, Kiichi Urahama

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)374-377
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE90-D
Issue number1
DOIs
Publication statusPublished - Jan 1 2007

Fingerprint

Wine
Ionosphere
Discriminant analysis
Linearization
Logistics
Experiments

All Science Journal Classification (ASJC) codes

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

Cite this

Semi-supervised classification with spectral subspace projection of data. / Du, Weiwei; Urahama, Kiichi.

In: IEICE Transactions on Information and Systems, Vol. E90-D, No. 1, 01.01.2007, p. 374-377.

Research output: Contribution to journalArticle

Du, Weiwei ; Urahama, Kiichi. / Semi-supervised classification with spectral subspace projection of data. In: IEICE Transactions on Information and Systems. 2007 ; Vol. E90-D, No. 1. pp. 374-377.
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