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

Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding

Shu Liu, Zhengguang Chen, and Feng Jiao
Appl. Opt. 61(7) 1704-1710 (2022)

Feature reduction and morphological processing for hyperspectral image data

David Casasent and Xue-Wen Chen
Appl. Opt. 43(2) 227-236 (2004)

Testing different classification methods in airborne hyperspectral imagery processing

Vladimir V. Kozoderov and Egor V. Dmitriev
Opt. Express 24(10) A956-A965 (2016)

Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images

Xiaoqing Wan and Chunhui Zhao
J. Opt. Soc. Am. A 34(6) 1011-1020 (2017)

Use of customizing kernel sparse representation for hyperspectral image classification

Bin Qi, Chunhui Zhao, and Guisheng Yin
Appl. Opt. 54(4) 707-716 (2015)