HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Locally linear generative topographic mapping

Abstract : We propose a method for non-linear data pro- jection that combines Generative Topographic Mapping and Coordinated PCA. We extend the Generative Topographic Mapping by using more complex nodes in the network: each node provides a linear map between the data space and the latent space. The location of a node in the data space is given by a smooth non-linear function of its location in the latent space. Our model provides a piece-wise linear mapping between data and latent space, as opposed to the point-wise coupling of the Generative Topographic Mapping. We provide experimental results comparing this model with GTM.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download


https://hal.inria.fr/inria-00321501
Contributor : Jakob Verbeek Connect in order to contact the contributor
Submitted on : Wednesday, February 16, 2011 - 5:11:34 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on: : Tuesday, May 17, 2011 - 2:33:22 AM

Files

verbeek02bnl.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00321501, version 1

Citation

Jakob Verbeek, Nikos Vlassis, Ben Krose. Locally linear generative topographic mapping. Benelearn: Annual Machine Learning Conference of Belgium and the Netherlands, Dec 2002, Utrecht, Netherlands. pp.79--86. ⟨inria-00321501⟩

Share

Metrics

Record views

115

Files downloads

258