Abstract: In order to effectively extract nonlinear features of data set, the paper proposed a new method, called Kernel Semi-supervised Locality Preserving Projection (KSSLPP). It redefined the between-class similarity and within-class similarity using rich labeled and unlabeled samples that contain valuable information, which was used to maximize the between-class separability and minimize the within-class separability in a high dimensional kernel space. The proposed method preserves the global and local structures of unlabeled samples in addition to separating labeled samples in different classes. Contrast experiments in the Olivetti face database and UCI database verify the effectiveness of the proposed algorithm.

Key words: dimensionality reduction, semi-supervised, kernel method, local data structure, global data structure

摘要： 为能有效捕捉数据的非线性特征，特提出一种新的非线性数据降维算法——核半监督局部保留投影(KSSLPP)。该方法利用标记样本的标记信息及所有训练样本的结构重新定义了类间相似度和类内相似度，然后将原始数据映射到高维核空间，在核空间中最大化类间分离度，最小化类内分离度。该方法在核空间保持了数据的局部结构和全局结构，以及数据的标签信息。在Olivetti人脸库和UCI数据库中的对比实验验证了该算法的有效性。

关键词: 数据降维, 半监督, 核方法, 局部结构, 全局结构