Bayesian kernel projection classifier (BKPC) is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.
The main function is bkpc.
The data can be passed to the bkpc function as:
a matrix of features,
a kernel matrix of either:
class ‘kern’ (a Gaussian kernel computed using the gaussKern{BKPC} function) or
class ‘kernelMatrix’ from library kernlab. This allows for a wider selection of inbuilt kernel generating functions as well as user defined functions.
The package contains a microarray dataset and a function to extract the marginal relevance of each feature for classification.
See ?bkpc and ?marginalRelevance