K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space.
Max BylesjöMattias RantalainenJeremy K. NicholsonElaine HolmesJohan TryggPublished in: BMC Bioinform. (2008)
Keyphrases
- feature space
- kernel methods
- prediction accuracy
- kernel pca
- support vector machine
- high dimensional
- prediction model
- training samples
- dimensionality reduction
- input space
- high dimensionality
- prediction algorithm
- linear discriminant analysis
- kernel trick
- training set
- image interpretation
- prediction error
- mean shift
- latent space
- mercer kernel
- dimension reduction
- principal component analysis
- feature vectors
- machine learning
- visual words
- image representation
- input data
- feature set
- data points
- radon transform
- high level
- three dimensional
- feature selection
- parallel lines
- experimentally determined
- neural network