nmf-torch 0.1.1

Advanced Settings

By default, run_nmf function uses the batch HALS solver for NMF decomposition. In total, there are other solvers available in NMF-torch:

• HALS: Hierarchical Alternative Least Squares ([Kimura et al., 2015]). The default.

• MU: Multiplicative Update. Set algo='mu' in run_nmf function.

• BPP: Alternative non-negative least squares with Block Principal Pivoting method ([Kim & Park, 2011]). Set algo='bpp' in run_nmf function.

Besides, each solver has two modes: batch and online. The online mode is a modified version which is scalable for input matrix of a large number of samples. You can set mode='online' in run_nmf function to switch to use the online mode.

The default beta loss is Frobenius (L2) distance, which is the most commonly used. By changing beta_loss parameter in run_nmf function, users can specify other beta loss metrics:

• KL divergence: beta_loss='kullback-leibler' or beta_loss=1.0;

• Itakura-Saito divergence: beta_loss='itakura-saito' or beta_loss=0;

• Or any non-negative float number.

Notice that since online mode only works for L2 loss, if you specify other beta loss, run_nmf will automatically switch back to batch mode.

For the other parameters in run_nmf function, please type help(run_nmf) in your Python interpreter to view.

Advanced Settings

Similarly as in run_nmf function above, integrative_nmf provides 2 modes (batch and online) and 3 solvers: HALS, MU, and BPP. By default, batch HALS is used. You can switch to other solvers and modes by specifying algo and mode parameters.

There is another important parameter lam for the coefficient for regularization terms, with default value 5.0. If set to 0, then no regularization will be applied.

Notice that only L2 loss is accepted in iNMF.

For the other parameters in integrative_nmf function, please type help(integrative_nmf) in your Python interpreter to view.