Non-negative matrix factorization or NNMF
Alternating algorithm converges to a local minimum of
Pros
- Natural fit for positive-valued data
- Can be interpreted (meaningful signs)
Cons
- Only applicable to non-negative data
- Optimization procedure is non-convex; requires initialization
- “Interpretability” is unreliable (reducing can completely change the basis function rather than select a subset)
- Learned components are not orthogonal nor naturally ordered