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A brief comparison of specialties of PCA, ICA, and NMF.
From the intuition behind the mathematical formulation and the results given by our chosen dataset, we reach the conclusion that different matrix factorization techniques work well towards different ends:
NMF works well for modeling non-negative data such as images. It finds sparse and parts-based representation of the data.
ICA works well for finding independent sources when the data isn’t Gaussian.
PCA has much broader application than ICA and NMF; it is ideal for pattern recognition and dimension reduction.
Read also:
OpenStax, Comparison of three different matrix factorization techniques for unsupervised machine learning. OpenStax CNX. Dec 18, 2013 Download for free at http://cnx.org/content/col11602/1.1
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