scripts/builtin/gnmf.dml

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# The gnmf-function does Gaussian Non-Negative Matrix Factorization. In this, a matrix X is factorized into two
# matrices W and H, such that all three matrices have no negative elements. This non-negativity makes the resulting
# matrices easier to inspect.
#
# References:
# [Chao Liu, Hung-chih Yang, Jinliang Fan, Li-Wei He, Yi-Min Wang:
# Distributed nonnegative matrix factorization for web-scale dyadic
# data analysis on mapreduce. WWW 2010: 681-690]
#
# INPUT:
# --------------------------------------------------------------------------------------
# X      Matrix of feature vectors.
# rnk    Number of components into which matrix X is to be factored
# eps    Tolerance
# maxi   Maximum number of conjugate gradient iterations
# --------------------------------------------------------------------------------------
#
# OUTPUT:
# --------------------------------------------------------------------------------------
# W     List of pattern matrices, one for each repetition
# H     List of amplitude matrices, one for each repetition
# --------------------------------------------------------------------------------------

m_gnmf = function(Matrix[Double] X, Integer rnk, Double eps = 1e-8, Integer maxi = 10)
  return (Matrix[Double] W, Matrix[Double] H) 
{
  #initialize W and H
  W = rand(rows=nrow(X), cols=rnk, min=-0.05, max=0.05);
  H = rand(rows=rnk, cols=ncol(X), min=-0.05, max=0.05);

  i = 0;
  while(i < maxi) {
    H = H * ((t(W) %*% X) / (((t(W) %*% W) %*% H)+eps));
    W = W * ((X %*% t(H)) / ((W %*% (H %*% t(H)))+eps));
    i = i + 1;
  }
}