2005 IEEE International Conference on Multimedia and Expo Supervised semi-definite embedding for image manifolds Amsterdam, Netherlands July 06-July 06 ISBN: 0-7803-9331-7
Semi-definite embedding (SDE) has been a recently proposed to maximize the sum of pair wise squared distances between outputs while the input data and outputs are locally isometric, i.e. it pulls the outputs as far apart as possible, subject to unfolding a manifold without any furling or fold for unsupervised nonlinear dimensionality reduction. The extensions of SDE to supervised feature extraction, named as supervised Semi-definite embedding (SSDE) was proposed by the authors of this paper. Here, the method is unified in a mathematical framework and applied to a number of benchmark data sets. Results show that SSDE performs very well on high-dimensional data, which exhibits a manifold structure.
Index Terms:
high-dimensional data, supervised semidefinite embedding, SSDE, image manifold, nonlinear dimensional reduction, feature extraction, benchmark data set
Citation:
null Benyu Zhang, null Jun Yan, null Ning Liu, null Qiansheng Cheng, null Zheng Chen, null Wei-Ying Ma, "Supervised semi-definite embedding for image manifolds," icme, pp.4 pp., 2005 IEEE International Conference on Multimedia and Expo, 2005 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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