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Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 6, Iss. 8 — Aug. 10, 2008
  • pp: 558–560

Shrinkage-divergence-proximity locally linear embedding algorithm for dimensionality reduction of hyperspectral image

Qin Luo, Zheng Tian, and Zhixiang Zhao  »View Author Affiliations


Chinese Optics Letters, Vol. 6, Issue 8, pp. 558-560 (2008)


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Abstract

Existing manifold learning algorithms use Euclidean distance to measure the proximity of data points. However, in high-dimensional space, Minkowski metrics are no longer stable because the ratio of distance of nearest and farthest neighbors to a given query is almost unit. It will degrade the performance of manifold learning algorithms when applied to dimensionality reduction of high-dimensional data. We introduce a new distance function named shrinkage-divergence-proximity (SDP) to manifold learning, which is meaningful in any high-dimensional space. An improved locally linear embedding (LLE) algorithm named SDP-LLE is proposed in light of the theoretical result. Experiments are conducted on a hyperspectral data set and an image segmentation data set. Experimental results show that the proposed method can efficiently reduce the dimensionality while getting higher classification accuracy.

© 2008 Chinese Optics Letters

OCIS Codes
(070.4340) Fourier optics and signal processing : Nonlinear optical signal processing
(100.0100) Image processing : Image processing
(100.5010) Image processing : Pattern recognition

Citation
Qin Luo, Zheng Tian, and Zhixiang Zhao, "Shrinkage-divergence-proximity locally linear embedding algorithm for dimensionality reduction of hyperspectral image," Chin. Opt. Lett. 6, 558-560 (2008)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-6-8-558


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