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 ...
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...
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
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...
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 ...