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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 106867, 14 pages
Research Article

Fast Discriminative Stochastic Neighbor Embedding Analysis

School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 9 February 2013; Accepted 22 March 2013

Academic Editor: Carlo Cattani

Copyright © 2013 Jianwei Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding analysis (FDSNE) method for feature extraction is proposed in this paper by improving the existing DSNE method. The proposed algorithm adopts an alternative probability distribution model constructed based on its -nearest neighbors from the interclass and intraclass samples. Furthermore, FDSNE is extended to nonlinear scenarios using the kernel trick and then kernel-based methods, that is, KFDSNE1 and KFDSNE2. FDSNE, KFDSNE1, and KFDSNE2 are evaluated in three aspects: visualization, recognition, and elapsed time. Experimental results on several datasets show that, compared with DSNE and MSNP, the proposed algorithm not only significantly enhances the computational efficiency but also obtains higher classification accuracy.