A latent variable model based on local preservation

作者：Wang Xiu Mei^*; Gao Xin Bo; Zhang Qian Kun; Song Guo Xiang

来源：Pattem Recognition and Aitificial Intelligence, 2010, 23(3): 369-375.

• Close-in
• Constrained terms
• Data sets
• Data space
• Dimensionality reduction
• Gaussian Processes
• Kernel mapping
• Latent variable models
• Local structure
• Locality preserving projections
• Nonlinear dimensionality reduction
• Objective functions

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.

• 出版日期2010
• 单位西安电子科技大学