2005-11-28-high-dimensional-probabilistic-modelling-through-manifolds.md

abstract	author	categories	day	errata	extras	group	key	layout	linkpptgz	month	published	section	title	venue	year
Density modelling in high dimensions is a very difficult problem. Traditional approaches, such as mixtures of Gaussians, typically fail to capture the structure of data sets in high dimensional spaces. In this talk we will argue that for many data sets of interest, the data can be represented as a lower dimensional manifold immersed in the higher dimensional space. We will then present the Gaussian Process Latent Variable Model (GP-LVM), a non-linear probabilistic variant of principal component analysis (PCA) which implicitly assumes that the data lies on a lower dimensional space. We will demonstrate the application of the model to a range of data sets, but with a particular focus on human motion data. We will show some preliminary work on facial animation and make use of a skeletal motion capture data set to illustrate differences between our model and traditional manifold techniques.	family given gscholar institute twitter url Lawrence Neil D. r3SJcvoAAAAJ University of Sheffield lawrennd http://inverseprobability.com	Lawrence--ibm05	28			gplvm	Lawrence--ibm05	talk	ftp://ftp.dcs.shef.ac.uk/home/neil/gplvm_05_11.ppt.gz	11	2005-11-28	pre	High Dimensional Probabilistic Modelling through Manifolds	IBM Thomas J Watson Research Center, New York, U.S.A.	2005