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» Topologically-constrained latent variable models
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DAGM
2010
Springer
13 years 11 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
JMLR
2010
93views more  JMLR 2010»
13 years 4 months ago
Distinguishing between cause and effect
We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y . The basic idea is to model the observed data using probabilistic late...
Joris M. Mooij, Dominik Janzing
ICCS
2007
Springer
14 years 1 months ago
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was m...
Nevin Lianwen Zhang
ICASSP
2009
IEEE
13 years 7 months ago
Probabilistic matrix tri-factorization
Nonnegative matrix tri-factorization (NMTF) is a 3-factor decomposition of a nonnegative data matrix, X USV , where factor matrices, U, S, and V , are restricted to be nonnegativ...
Jiho Yoo, Seungjin Choi
ICML
2007
IEEE
14 years 10 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore