Local distance preservation in the GP-LVM through back constraints


The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to data space. It is also a non-linear generalization of probabilistic PCA (PPCA) (Tipping & Bishop, 1999). While most approaches to non-linear dimensionality methods focus on… (More)
DOI: 10.1145/1143844.1143909


5 Figures and Tables


Citations per Year

235 Citations

Semantic Scholar estimates that this publication has 235 citations based on the available data.

See our FAQ for additional information.