Graphs ensemble based locality preserving projection
Yu Guoxian · Jisuanji gongcheng yu sheji · 2010
Locality preserving projection(LPP) is sensitive to noise and its effectiveness relies much on the construction of neighborhood graph.To tackle these problems,a graphs ensemble based locality preserving projection(GELPP) method is proposed.GELPP first derives robust similarity between samples via robust statistic.Secondly,it constructs several approximate maximum spanning trees(MST) via this similarity,then combines these trees into an ensemble graph by the virtues of strong generalization ability of ensemble learning.Finally,it replaces similarity metric and neighborhood graph of LPP with robust similarity and this ensemble graph respectively.Experiments on high dimensional face databases prove not only GELPP’s robustness to noise and outliers,but also its effectiveness in dimensionality reduction on graphs ensemble.