01388nas a2200181   4500008004100000245006200041210006200103260000900165300001400174490000700188520072900195653002900924653003900953653003000992653005601022100001101078856011701089       2013                        eng d00aRegularized orthogonal local fisher discriminant analysis0 aRegularized orthogonal local fisher discriminant analysis  c2013  a154 - 1590 v113 aAiming 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.10aDimensionality reduction10aLocal fisher discriminant analysis10aLocal nonlinear structure10aRegularized orthogonal linear discriminant analysis1 aXu, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879121480&partnerID=40&md5=294ec31f3b8631180e347dff9cae6b78