Corrigendum to "Unsupervised learning via mixtures of skewed distributions with hypercube contours" [Pattern Recognition Letters. 58(1), 69-76].
Brian C. FranczakCristina TortoraRyan P. BrownePaul D. McNicholasPublished in: Pattern Recognit. Lett. (2015)
Keyphrases
- unsupervised learning
- pattern recognition
- mixture model
- dimensionality reduction
- heavy tailed distributions
- heavy tailed
- highly skewed
- expectation maximization
- supervised learning
- machine learning
- semi supervised
- probability distribution
- image analysis
- mixture distribution
- signal processing
- neural network
- computer vision
- image processing
- class distribution
- deep learning
- feature extraction
- pattern recognition problems
- model selection
- mixture components
- random variables
- object recognition
- gaussian distribution
- gaussian mixture
- deep architectures
- master slave
- em algorithm
- mixture distributions
- mixtures of gaussians
- dirichlet process
- probability density
- gaussian mixture model
- data distribution
- support vector machine svm
- active contours
- three dimensional
- power law
- pattern classification
- covariance matrix
- learning algorithm
- feature space
- maximum likelihood
- feature vectors