AI engineering glossary · Classical ML algorithms
What is NMF (non-negative matrix factorisation)?
Non-negative matrix factorisation (also called NMF): A method that splits a matrix of non-negative values into two smaller non-negative matrices whose product approximates it. Because parts can only add up, factors tend to be interpretable, such as topics or parts of images.
Non-negative matrix factorisation: an example
Factorising a document-word count matrix into topics, each a set of words with positive weights.
Related terms
More Classical ML algorithms terms
- Normal equation
- Odds ratio
- One-class SVM
- One-vs-one
- One-vs-rest
- Ordinary least squares
- Out-of-bag error
- Perceptron
Where you learn it
Non-negative matrix factorisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.