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Conference Papers Year : 2002

Locally linear generative topographic mapping

Jakob Verbeek
Nikos Vlassis
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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.
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Dates and versions

inria-00321501 , version 1 (16-02-2011)


  • HAL Id : inria-00321501 , version 1


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⟩
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