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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.

 

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