@article {382,
	title = {Regularized orthogonal local fisher discriminant analysis},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {154 - 159},
	abstract = {Aiming at deficiencies of the ability for preserving local nonlinear structure of recently proposed Regularized Orthogonal Linear Discriminant Analysis (ROLDA) for dimensionality reduction, a kind of dimensionality reduction algorithm named Regularized Orthogonal Local Fisher Discriminant Analysis (ROLFDA) is proposed in the paper, which is originated from ROLDA. The algorithm introduce the idea of local structure preserving in Local Fisher Discriminant Analysis (LFDA) on the basic of ROLDA, following properties of ROLDA and strengthening the ability for capturing local structure information of data with nonlinear structures. Experiments on real face datasets demonstrate the effectiveness of our proposed algorithm.},
	keywords = {Dimensionality reduction, Local fisher discriminant analysis, Local nonlinear structure, Regularized orthogonal linear discriminant analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879121480\&partnerID=40\&md5=294ec31f3b8631180e347dff9cae6b78},
	author = {Xu, S.}
}
