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 preserving local distances in data space, the GP-LVM focusses on exactly the opposite. Being a smooth mapping from latent to data space, it focusses on keeping things apart in latent space that are far apart in data space. In this paper we first provide an overview of dimensionality reduction techniques, placing the emphasis on the kind of distance relation preserved. We then show how the GP-LVM can be generalized, through back constraints, to additionally preserve local distances. We give illustrative experiments on common data sets.

DOI: 10.1145/1143844.1143909

Extracted Key Phrases

5 Figures and Tables

Citations per Year

222 Citations

Semantic Scholar estimates that this publication has 222 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Lawrence2006LocalDP, title={Local distance preservation in the GP-LVM through back constraints}, author={Neil D. Lawrence and Joaquin Qui{\~n}onero Candela}, booktitle={ICML}, year={2006} }