randomized_svd.hpp

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#ifndef MLPACK_METHODS_RANDOMIZED_SVD_RANDOMIZED_SVD_HPP
#define MLPACK_METHODS_RANDOMIZED_SVD_RANDOMIZED_SVD_HPP
 
#include <mlpack/prereqs.hpp>
 
namespace mlpack {
namespace svd {
 
class RandomizedSVD
{
 public:
  RandomizedSVD(const arma::mat& data,
                arma::mat& u,
                arma::vec& s,
                arma::mat& v,
                const size_t iteratedPower = 0,
                const size_t maxIterations = 2,
                const size_t rank = 0,
                const double eps = 1e-7);
 
  RandomizedSVD(const size_t iteratedPower = 0,
                const size_t maxIterations = 2,
                const double eps = 1e-7);
 
  void Apply(const arma::sp_mat& data,
             arma::mat& u,
             arma::vec& s,
             arma::mat& v,
             const size_t rank);
 
  void Apply(const arma::mat& data,
             arma::mat& u,
             arma::vec& s,
             arma::mat& v,
             const size_t rank);
 
  template<typename MatType>
  void Apply(const MatType& data,
             arma::mat& u,
             arma::vec& s,
             arma::mat& v,
             const size_t rank,
             MatType rowMean)
  {
    if (iteratedPower == 0)
      iteratedPower = rank + 2;
 
    arma::mat R, Q, Qdata;
 
    // Apply the centered data matrix to a random matrix, obtaining Q.
    if (data.n_cols >= data.n_rows)
    {
      R = arma::randn<arma::mat>(data.n_rows, iteratedPower);
      Q = (data.t() * R) - arma::repmat(arma::trans(R.t() * rowMean),
          data.n_cols, 1);
    }
    else
    {
      R = arma::randn<arma::mat>(data.n_cols, iteratedPower);
      Q = (data * R) - (rowMean * (arma::ones(1, data.n_cols) * R));
    }
 
    // Form a matrix Q whose columns constitute a
    // well-conditioned basis for the columns of the earlier Q.
    if (maxIterations == 0)
    {
      arma::qr_econ(Q, v, Q);
    }
    else
    {
      arma::lu(Q, v, Q);
    }
 
    // Perform normalized power iterations.
    for (size_t i = 0; i < maxIterations; ++i)
    {
      if (data.n_cols >= data.n_rows)
      {
        Q = (data * Q) - rowMean * (arma::ones(1, data.n_cols) * Q);
        arma::lu(Q, v, Q);
        Q = (data.t() * Q) - arma::repmat(rowMean.t() * Q, data.n_cols, 1);
      }
      else
      {
        Q = (data.t() * Q) - arma::repmat(rowMean.t() * Q, data.n_cols, 1);
        arma::lu(Q, v, Q);
        Q = (data * Q) - (rowMean * (arma::ones(1, data.n_cols) * Q));
      }
 
      // Computing the LU decomposition is more efficient than computing the QR
      // decomposition, so we only use it in the last iteration, a pivoted QR
      // decomposition which renormalizes Q, ensuring that the columns of Q are
      // orthonormal.
      if (i < (maxIterations - 1))
      {
        arma::lu(Q, v, Q);
      }
      else
      {
        arma::qr_econ(Q, v, Q);
      }
    }
 
    // Do economical singular value decomposition and compute only the
    // approximations of the left singular vectors by using the centered data
    // applied to Q.
    if (data.n_cols >= data.n_rows)
    {
      Qdata = (data * Q) - rowMean * (arma::ones(1, data.n_cols) * Q);
      arma::svd_econ(u, s, v, Qdata);
      v = Q * v;
    }
    else
    {
      Qdata = (Q.t() * data) - arma::repmat(Q.t() * rowMean, 1,  data.n_cols);
      arma::svd_econ(u, s, v, Qdata);
      u = Q * u;
    }
  }
 
  size_t IteratedPower() const { return iteratedPower; }
  size_t& IteratedPower() { return iteratedPower; }
 
  size_t MaxIterations() const { return maxIterations; }
  size_t& MaxIterations() { return maxIterations; }
 
  double Epsilon() const { return eps; }
  double& Epsilon() { return eps; }
 
 private:
  size_t iteratedPower;
 
  size_t maxIterations;
 
  double eps;
};
 
} // namespace svd
} // namespace mlpack
 
#endif