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Optical Engineering

Novel semisupervised high-dimensional correspondences learning method
Author(s): Chenping Hou; Yi Wu; Dongyun Yi; Yuanyuan Jiao
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Paper Abstract

Correspondence is one of the big challenges in machine learning and image processing. To match two high-dimensional data sets with a certain number of aligned training examples, a novel semisupervised method is proposed. It is mainly based on two manifold learning approaches: maximum variance unfolding (MVU) and locally linear embedding (LLE). We have modified MVU to a semi-supervised version to solve the correspondence problem. Additionally, the nonuniform warps and folds caused by employing LLE alone and the computational burden of MVU disappear when they are combined. The proposed algorithm outperforms traditional methods in accuracy and efficiency. Three examples are performed to demonstrate the potential of this method.

Paper Details

Date Published: 1 April 2008
PDF: 10 pages
Opt. Eng. 47(4) 047201 doi: 10.1117/1.2903093
Published in: Optical Engineering Volume 47, Issue 4
Show Author Affiliations
Chenping Hou, National Univ. of Defense Technology (China)
Yi Wu, National Univ. of Defense Technology (China)
Dongyun Yi, National Univ. of Defense Technology (China)
Yuanyuan Jiao, National Univ. of Defense Technology (China)

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