Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models.
Noam SiegelmanMark van den BuntJason Chor Ming LoJay G. RuecklKenneth R. PughPublished in: NeuroImage (2021)
Keyphrases
- mixture model
- density estimation
- model selection
- maximum likelihood
- unsupervised learning
- gaussian mixture model
- em algorithm
- generative model
- finite mixture models
- probability density function
- pattern recognition
- mixture modeling
- feature vectors
- probabilistic model
- decision trees
- machine learning
- dirichlet prior
- probabilistic mixture model
- subspace analysis
- feature extraction
- supervised learning
- feature space
- automatic model selection
- data mining
- support vector
- gaussian mixture
- expectation maximization
- model based clustering
- image processing
- variational inference
- data sets
- generalized em algorithm
- estimate the model parameters
- bayesian networks
- posterior probability
- object recognition
- class labels
- feature set
- language model