Gaussian mixtures (GMs) are widely used in signal processing applications to capture the
multimodal behavior of dynamic systems. Due to an exponential increase in the number of …

The learning of variational inference can be widely seen as first estimating the class
assignment variable and then using it to estimate parameters of the mixture model. The …

Large scale Bayesian nonparametrics (BNP) learner such as stochastic variational inference
(SVI) can handle datasets with large class number and large training size at fractional cost …

In Bayesian nonparametrics model such as Dirichlet process mixture (DPM), learning is
almost exclusive to either variational inference or Gibbs sampling. Yet variational inference …

New visual and static features, namely, right singular feature vector, left singular feature
vector and singular value feature vector are proposed for the semantic concept detection in …

Object recognition is one of the key areas in computer vision, which aims to help computer to
see the world in a way similar to the capability of a human. Consequentially, the design of a …

This thesis mainly propose variational inference for Bayesian mixture models and their
applications to solve machine learning problems. The mixture models addressed are the …

Corrections to “Sparse Coding Based Fisher Vector Using a Bayesian Approach” | IEEE
Journals & Magazine | IEEE Xplore Corrections to “Sparse Coding Based Fisher Vector Using a …