Abstract：Latent variable model (LVM) is a kind of efficient nonlinear dimensionality reduction algorithm through establishing smooth kernel mappings from the latent space to the data space. However, this kind of mappings cannot keep the points close in the latent space even they are close in data space. A LVM is proposed based on locality preserving projection (LPP) which can preserve the locality structure of dataset. The objective function of LPP is considered as a prior of the variables in the Gaussian process latent variable model (GP-LVM). The proposed locality preserving GP-LVM is built with the constrained term of the objective function. Compared with the traditional LPP and GP-LVM, experimental results show that the proposed method performs better in preserving local structure on common data sets.
王秀美，高新波，张乾坤，宋国乡. 一种基于局部保持的隐变量模型[J]. 模式识别与人工智能, 2010, 23(3): 369-375.
WANG Xiu-Mei,GAO Xin-Bo,ZHANG Qian-Kun,SONG Guo-Xiang. A Latent Variable Model Based on Local Preservation. , 2010, 23(3): 369-375.
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