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稿件标题: A review on Gaussian Process Latent Variable Models
稿件作者: Ping Li, Songcan Chen
关键字词: GPLVM; Non-parametric method; Gaussian process
文章摘要: Gaussian Process Latent Variable Model (GPLVM), as a flexible bayesian non-parametric modeling method, has been extensively studied and applied in many learning tasks such as Intrusion Detection, Image Reconstruction, Facial Expression Recognition, Human pose estimation and so on. In this paper, we give a review and analysis for GPLVM and its extensions. Firstly, we formulate basic GPLVM and discuss its relation to Kernel Principal Components Analysis. Secondly, we summarize its improvements or variants and propose a taxonomy of GPLVM related models in terms of the various strategies that be used. Thirdly, we provide the detailed formulations of the main GPLVMs that extensively developed based on the strategies described in the paper. Finally, we further give some challenges in next researches of GPLVM.
收录刊物: 2016年1卷4期
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