A loss function approach to model selection in nonlinear principal components.
Andrew R. WebbPublished in: Neural Networks (1999)
Keyphrases
- model selection
- loss function
- principal components
- kernel principal component analysis
- principal component analysis
- cross validation
- support vector
- parameter estimation
- pairwise
- sample size
- hyperparameters
- dimensionality reduction
- logistic regression
- regression model
- machine learning
- gaussian process
- variable selection
- model selection criteria
- covariance matrix
- feature selection
- mixture model
- feature set
- hyperplane
- generalization error
- low dimensional
- reproducing kernel hilbert space
- independent component analysis
- training set
- generalization bounds