Semi-supervised Laplacian eigenmaps for dimensionality reduction

Dimensionality reduction with prior information is considered. The semi-supervised Laplacian eigenmap algorithm is proposed. It is shown that the performance of dimensionality reduction algorithms can be improved by taking into account the label information of the data. The data analysis and experim...

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Published in2008 International Conference on Wavelet Analysis and Pattern Recognition Vol. 2; pp. 843 - 849
Main Authors Feng Zheng, Na Chen, Luoqing Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Conferences
Label information
Laplacian eigenmaps
Manifold learning
Pattern recognition
Wavelet analysis
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ISBN9781424422388
1424422388
ISSN2158-5695
DOI10.1109/ICWAPR.2008.4635894

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Abstract Dimensionality reduction with prior information is considered. The semi-supervised Laplacian eigenmap algorithm is proposed. It is shown that the performance of dimensionality reduction algorithms can be improved by taking into account the label information of the data. The data analysis and experiments show the validity of our algorithm.
AbstractList Dimensionality reduction with prior information is considered. The semi-supervised Laplacian eigenmap algorithm is proposed. It is shown that the performance of dimensionality reduction algorithms can be improved by taking into account the label information of the data. The data analysis and experiments show the validity of our algorithm.
Author Na Chen
Luoqing Li
Feng Zheng
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Snippet Dimensionality reduction with prior information is considered. The semi-supervised Laplacian eigenmap algorithm is proposed. It is shown that the performance...
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SubjectTerms Conferences
Label information
Laplacian eigenmaps
Manifold learning
Pattern recognition
Wavelet analysis
Title Semi-supervised Laplacian eigenmaps for dimensionality reduction
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Volume 2
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