nystroem_method.hpp

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#ifndef MLPACK_METHODS_KERNEL_PCA_NYSTROEM_METHOD_HPP
#define MLPACK_METHODS_KERNEL_PCA_NYSTROEM_METHOD_HPP
 
#include <mlpack/prereqs.hpp>
#include <mlpack/methods/nystroem_method/kmeans_selection.hpp>
#include <mlpack/methods/nystroem_method/nystroem_method.hpp>
 
namespace mlpack {
namespace kpca {
 
template<
  typename KernelType,
  typename PointSelectionPolicy = kernel::KMeansSelection<>
>
class NystroemKernelRule
{
 public:
  static void ApplyKernelMatrix(const arma::mat& data,
                                arma::mat& transformedData,
                                arma::vec& eigval,
                                arma::mat& eigvec,
                                const size_t rank,
                                KernelType kernel = KernelType())
  {
    arma::mat G, v;
    kernel::NystroemMethod<KernelType, PointSelectionPolicy> nm(data, kernel,
        rank);
    nm.Apply(G);
    transformedData = G.t() * G;
 
    // Center the reconstructed approximation.
    math::Center(transformedData, transformedData);
 
    // For PCA the data has to be centered, even if the data is centered. But
    // it is not guaranteed that the data, when mapped to the kernel space, is
    // also centered. Since we actually never work in the feature space we
    // cannot center the data. So, we perform a "psuedo-centering" using the
    // kernel matrix.
    arma::colvec colMean = arma::sum(G, 1) / G.n_rows;
    G.each_row() -= arma::sum(G, 0) / G.n_rows;
    G.each_col() -= colMean;
    G += arma::sum(colMean) / G.n_rows;
 
    // Eigendecompose the centered kernel matrix.
    transformedData = arma::symmatu(transformedData);
    if (!arma::eig_sym(eigval, eigvec, transformedData))
    {
      Log::Fatal << "Failed to construct the kernel matrix." << std::endl;
    }
 
    // Swap the eigenvalues since they are ordered backwards (we need largest
    // to smallest).
    for (size_t i = 0; i < floor(eigval.n_elem / 2.0); ++i)
      eigval.swap_rows(i, (eigval.n_elem - 1) - i);
 
    // Flip the coefficients to produce the same effect.
    eigvec = arma::fliplr(eigvec);
 
    transformedData = eigvec.t() * G.t();
  }
};
 
} // namespace kpca
} // namespace mlpack
 
#endif