Improved neighborhood preserving embedding approach
Ruicong Zhi, Qiuqi Ruan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.