DocumentCode
2989430
Title
Semi-supervised Laplacian eigenmaps for dimensionality reduction
Author
Zheng, Feng ; Chen, Na ; Li, Luoqing
Author_Institution
Fac. of Math. & Comput. Sci., Hubei Univ., Wuhan
Volume
2
fYear
2008
fDate
30-31 Aug. 2008
Firstpage
843
Lastpage
849
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.
Keywords
Laplace equations; data analysis; data mining; data reduction; eigenvalues and eigenfunctions; graph theory; learning (artificial intelligence); matrix algebra; data analysis; dimensionality reduction algorithm; machine learning; semisupervised Laplacian eigenmap algorithm; weight matrix; weighted neighborhood graph; Laplace equations; Pattern analysis; Pattern recognition; Wavelet analysis; Label information; Laplacian eigenmaps; Manifold learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-2238-8
Electronic_ISBN
978-1-4244-2239-5
Type
conf
DOI
10.1109/ICWAPR.2008.4635894
Filename
4635894
Link To Document