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Digital Library of the
European Council for Modelling and Simulation |
Title: |
Kernel-Based Manifold-Oriented
Stochastic Neighbor Projection Method |
Authors: |
Jianwei Zheng, Hong Qiu, Qiongfang Huang, Wanliang Wang,
Xinli Xu |
Published in: |
(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H. Zhang European Council for Modeling
and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund, Norway, May 27th –
30th, 2013 |
Citation
format: |
Jianwei Zheng,
Hong Qiu, Qiongfang
Huang, Wanliang Wang, Xinli
Xu (2013). Kernel-Based Manifold-Oriented
Stochastic Neighbor Projection Method,
ECMS 2013 Proceedings edited by: W. Rekdalsbakken, R. T. Bye, H. Zhang, European Council for Modeling
and Simulation. doi:10.7148/2013-0843 |
DOI: |
http://dx.doi.org/10.7148/2013-0843 |
Abstract: |
A
new method for performing a nonlinear form of manifold-oriented stochastic
neighbor projection method is proposed. By the use of kernel functions, one
can operate in the feature space without ever computing the coordinates of
the data in that space, but rather by simply computing the inner products
between the images of all pairs of data in the feature space. The proposed
method is termed as kernel-based manifoldoriented stochastic
neighbor projection(KMSNP). By two different
strategies, KMSNP is divided into two methods: KMSNP1 and KMSNP2.
Experimental results on several databases show that, compared with the
relevant methods, the proposed methods obtain higher classification
performance and recognition rate. |
Full
text: |