Semi-supervised Locality Preserving Projection Dimensionality Reduction Method
Tan Rui, Xiuhong Chen · Jisuanji gongcheng · 2012
【Abstract】Existing algorithms can not effectively use rich labeled and unlabeled sample contains valuable information, which is useful for dimensionality reduction. Aiming at this problem, this paper proposes a novel method called Semi-supervised Locality Preserving Projection (SSLPP). It redefines the between-class similarity and within-class similarity, which is used to maximize the between-class separability and minimizes the within-class separability. In addition, the proposed method preserves the global and locality structure of unlabeled samples. Experimental results in artificial data sets, UCI databases and Olivetti face databases show the usefulness of SSLPP. 【Key words】data dimensionality reduction; semi-supervised; local structure; global structure; similarity; separability