In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixel...
Quang Anh Nguyen, Antonio Robles-Kelly, Chunhua Sh...
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 propose a new approach to anomaly detection by looking at the latent variable space to make the first step toward latent anomaly detection. Most conventional app...
We consider the problem of obtaining a reduced dimension representation of electropalatographic (EPG) data. An unsupervised learning approach based on latent variable modelling is...
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is bas...