Topologically-constrained latent variable models

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Topologically-constrained latent variable models
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean latent space. Our focus is the Gaussian Process latent variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable latent directions enabling, for example, style/content separation, and (b) generalise beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data.
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jo
Added 17 Nov 2009
Updated 13 Jul 2011
Type Conference
Year 2008
Where ICML
Authors Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence
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