This sprint will use Non-Negative Matrix factorization (NMF) to discover topics from our NYT corpus. Similar to kmeans and hierarchical clustering, NMF is a technique to help discover latent properties (features) in our data that a human might not have been able to see otherwise.
- Matrix factorization
- Dimensionality reduction
- Latent properties
- Linear combination of features
Exercise in pair.md.