关键词: 维数灾难, 特征抽取, 相似性度量, 鲁棒性

Abstract: Curse of dimensionality often comes out when learning from high dimensional data.Locality Preserving Projection（LPP） can preserve the local geometry of the manifold of data and solves this problem well.However，the similarity utilized in LPP is sensitive to outliers and noise，thus an Enhanced Locality Preserving Projection（ELPP） method is proposed with the robust path based similarity.Experiments on high dimensional manifold data prove the effectiveness of this method.

Key words: curse of dimensionality, feature extraction, similarity metric, robustness