Share Email Print
cover

Proceedings Paper

Improved neighborhood preserving embedding approach
Author(s): Ruicong Zhi; Qiuqi Ruan
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we proposed a manifold-based algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE) for dimensionality reduction and feature extraction. ONPE algorithm is based on the Neighborhood Preserving Embedding (NPE) algorithm. NPE is an unsupervised dimensionality reduction method which is the linear approximation of classical nonlinear method. However, the feature vectors obtained by NPE are nonorthogonal. ONPE inherits NPE's neighborhood preserving property and produces orthogonal feature vectors. As orthogonal eigenvectors preserve the metric structure of the image space, the ONPE algorithm has more neighborhood preserving power and discriminating power than NPE. Furthermore, ONPE can find the mapping which best preserves the manifold's estimated intrinsic geometry structure in a linear sense. Experimental results show that ONPE is an effective method for feature extraction.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880O (15 November 2007); doi: 10.1117/12.747709
Show Author Affiliations
Ruicong Zhi, Beijing Jiaotong Univ. (China)
Qiuqi Ruan, Beijing Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

© SPIE. Terms of Use
Back to Top