Non-negative Matrix Factorization

>>> from mlpack import nmf

This program performs non-negative matrix factorization on the given dataset, storing the resulting decomposed matrices in the specified files. For an input dataset V, NMF decomposes V into two matrices W and H such that

V = W * H

where all elements in W and H are non-negative. If V is of size (n x m), then W will be of size (n x r) and H will be of size (r x m), where r is the rank of the factorization (specified by the 'rank' parameter).

Optionally, the desired update rules for each NMF iteration can be chosen from the following list:

- multdist: multiplicative distance-based update rules (Lee and Seung 1999)

- multdiv: multiplicative divergence-based update rules (Lee and Seung 1999)

- als: alternating least squares update rules (Paatero and Tapper 1994)

The maximum number of iterations is specified with 'max_iterations', and the minimum residue required for algorithm termination is specified with the 'min_residue' parameter.

For example, to run NMF on the input matrix 'V' using the 'multdist' update rules with a rank-10 decomposition and storing the decomposed matrices into 'W' and 'H', the following command could be used:

>>> nmf(input=V, rank=10, update_rules='multdist')
>>> W = output['w']
>>> H = output['h']

input options

output options

The return value from the binding is a dict containing the following elements: