Machine Learning for OpenCV 4 - Second Edition

Implementing non-negative matrix factorization (NMF)

Another useful dimensionality reduction technique is called NMF. It again implements the same basic mathematical operations as PCA and ICA, but it has the additional constraint that it only operates on non-negative data. In other words, we cannot have negative values in our feature matrix if we want to use NMF; the resulting components of the decomposition will all have non-negative values as well.

In scikit-learn, NMF works exactly like ICA:

In [13]: nmf = decomposition.NMF()In [14]: X2 = nmf.fit_transform(X)In [15]: plt.plot(X2[:, 0], X2[:, 1], 'o')...      plt.xlabel('first non-negative component')...      plt.ylabel('second non-negative component')...      plt.axis([-5, 20, -5, 10])Out[15]: [-5, 20, ...