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Proceedings Paper

An algorithm of multi-structure based on Riemannian manifold learning
Author(s): Wei Wang; Du-yan Bi; Lei Xiong
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Paper Abstract

Riemannian Manifold Learning (RML) is a global algorithm proposed recently, so it can't preserve the local geometry property of neighboring data well. An algorithm of multi-structure based on RML is proposed in order to solve the problem. In the algorithm, all points were projected by PCA firstly so as to the extracted character is irrelevant, then constructed a neighbor graph. The most important step was that all data points were classified to two parts, for the k - NN of a base point, it adopted a weight which can preserve local property of the base point and neighboring nods to get the low-dimensional embedding coordinates. As for the other points, it still used the RML algorithm. Thus the new algorithm can both preserve the metrics at all scales and keep the geometrical property of local neighbor to the maximum. Experimental results on synthetic data and MNIST data set demonstrate that the new algorithm can reflect the intrinsic property better than the other manifold learning algorithms.

Paper Details

Date Published: 20 August 2010
PDF: 6 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202J (20 August 2010); doi: 10.1117/12.866982
Show Author Affiliations
Wei Wang, Univ. of Air Force Engineering (China)
Du-yan Bi, Univ. of Air Force Engineering (China)
Lei Xiong, Univ. of Air Force Engineering (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering

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