A review on Gaussian Process Latent Variable Models

Volume: 1, Issue: 4, Pages: 366 - 376
Published: Oct 1, 2016
Abstract
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...
Paper Details
Title
A review on Gaussian Process Latent Variable Models
Published Date
Oct 1, 2016
Volume
1
Issue
4
Pages
366 - 376
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.