PCA vs NMF

1)PCA and NMF optimize for a different result. 

2)PCA finds a new subspace which takes the same variance of the data and leads to a new feature. It is a dimension reduction method.

3)NMF finds nonnegative features of the given data, however one should be careful because NMF is very sensitive  to initialization, and hence won’t find the same features every time.

4)Output of NMF can be visualized as a smaller version of original dataset so that one would not have to deal with bigger dataset.

5) NMF is more useful most of the time Interpretability. The key is that all of the features learned via NMF are additive; that is, every point in the transformed space can be constructed by adding together strictly positive features.  (http://dx.doi.org/10.1109/IJCNN.2004.1381038)