Quantum mechanics

Gaussian

Process (computing)

Statistics

Latent variable

Operating system

Variable (mathematics)

Data mining

Computer science

Relation (database)

Physics

Kernel (algebra)

Pattern recognition (psychology)

Mathematics

Gaussian process

Mathematical analysis

Artificial intelligence

Parametric statistics

Combinatorics

Machine learning

Latent variable model

CAAI Transactions on Intelligence Technology

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.

A review on Gaussian Process Latent Variable Models