I am taking a course about using matrix factorization for machine learning.
The first thing that came into my mind is by using the matrix factorization we are always limited to linear relationships between the data, which is very limiting to predict complex patterns.
In comparison with neural networks, where we can use a non-linear activation function. It seems to me that all the tasks that matrix factorization can achieve will score better using a simple multilayer neural network.
So, can I conclude that NMF and matrix factorization for machine learning, in general, are not that practical, or there are cases where it's better to use NMF?