MCMC sampling on latent-variable space of mixture of probabilistic PCA.
Keisuke YamazakiPublished in: SCIS&ISIS (2012)
Keyphrases
- latent variable models
- latent variables
- posterior distribution
- markov chain monte carlo
- probabilistic model
- approximate inference
- latent space
- principal component analysis
- low dimensional
- monte carlo
- real valued
- mixture model
- generative model
- posterior probability
- gaussian process
- topic models
- dimensionality reduction
- metropolis hastings
- bayesian inference
- metropolis hastings algorithm
- lower dimensional
- sampling algorithm
- independent component analysis
- dirichlet process
- hidden variables
- feature extraction
- random variables
- expectation maximization
- graphical models
- markov chain
- parameter space
- high dimensional
- hidden markov models
- decision trees
- observed variables
- bayesian networks
- feature space
- discriminative learning
- gibbs sampling
- prior knowledge
- factor analysis
- high dimensional data
- learning problems
- latent dirichlet allocation
- data mining