Two-Dimensional Orthogonal Unsupervised Discriminant Projection
Unsupervised Discriminant Projection (UDP) is one of the most promising feature extraction methods. However, UDP suffers from the small sample size problem and the optimal basis vectors obtained by the UDP are nonorthogonal. In this paper, we present a new method called Two-dimensional Orthogonal Unsupervised Discriminant Projection (2DOUDP), which is not necessary to convert the image matrix into high-dimensional image vector and does not suffer the small sample size problem. To further improve the recognition performance, the orthogonal projection matrix obtained based on Gram–Schmidt orthogonalization is given. Experimental results on ORL database indicate that the proposed 2DOUDP method achieves better recognition rate than the UDP method and some other orthogonal feature extraction algorithms.
Runhua Tan, Jibing Sun and Qingsuo Liu
X. Z. Liang et al., "Two-Dimensional Orthogonal Unsupervised Discriminant Projection", Advanced Materials Research, Vols. 542-543, pp. 1343-1346, 2012