Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems
Table 1
Mean-SIRs [dB] obtained with 100 samples of Monte
Carlo analysis for the estimation of sources and columns of mixing matrix from
noise-free mixtures of signals in Figure 1. Sources are estimated with the projected
pseudoinverse. The number of inner iterations for updating is denoted by ,
and the number of layers (in the multilayer technique) by .
The notation best or worst in parenthesis that follows the
algorithm name means that the mean-SIR value is calculated for the best or
worst sample from Monte Carlo analysis, respectively. In the last column, the
elapsed time [in seconds] is given for each algorithm with and .
