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ECML
2005
Springer
14 years 7 days ago
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin ...
DAGM
2010
Springer
13 years 7 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
SDM
2008
SIAM
95views Data Mining» more  SDM 2008»
13 years 8 months ago
Deterministic Latent Variable Models and Their Pitfalls
We derive a number of well known deterministic latent variable models such as PCA, ICA, EPCA, NMF and PLSA as variational EM approximations with point posteriors. We show that the...
Max Welling, Chaitanya Chemudugunta, Nathan Sutter
ICCV
2007
IEEE
14 years 8 months ago
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
Shaobo Hou, Aphrodite Galata, Fabrice Caillette, N...
ICA
2012
Springer
12 years 2 months ago
New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem
In this contribution, new online EM algorithms are proposed to perform inference in general hidden Markov models. These algorithms update the parameter at some deterministic times ...
Sylvain Le Corff, Gersende Fort, Eric Moulines