DocumentCode :
3741893
Title :
Supervised Hessian Eigenmap for dimensionality reduction
Author :
Lianbo Zhang;Dapeng Tao; Weifeng Liu
Author_Institution :
College of Information and Control Engineering in China University of Petroleum(East China), Qingtao, Shandong, China
fYear :
2015
Firstpage :
903
Lastpage :
907
Abstract :
Hessian Eigenmap is a proposed technique for dimensionality reduction. Many methods, such as ISOMAP, LLE, Laplacian Eigenmap, have been proposed under manifold learning for dimensionality reduction. However, all these ideas have not taken the influence of different class into consideration, which limit the effectiveness of manifold learning. To take account for the influence for multiclass and improve the performance of dimensional reduction, we propose a new method, supervised Hessian LLE(SHLLE). To evaluate the proposed method, extensive experiments are conducted on the artificial dataset and real dataset (COIL-20). Our result demonstrate that the proposed method outperform HLLE method.
Keywords :
"Kernel","Analytical models"
Publisher :
ieee
Conference_Titel :
Communication Technology (ICCT), 2015 IEEE 16th International Conference on
Print_ISBN :
978-1-4673-7004-2
Type :
conf
DOI :
10.1109/ICCT.2015.7399970
Filename :
7399970
Link To Document :
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