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12. Algorithm Summary > Non-Negative Matrix Factorization

Non-Negative Matrix Factorization

Chapter 10 covered an advanced technique called non-negative matrix factorization (NMF), which is a way to break down a set of numerical observations into their component parts. This method was used to show how news stories could be composed of separate themes and how the trading volume of various stocks could be broken down into events that affected individual stocks or multiple stocks at once. This is also an unsupervised algorithm, since it helps characterize data rather than making predictions about categories or values.

To understand what NMF does, consider the set of values shown in Table 12-10:

Table 12-10. Simple table for NMF

Observation Number


  

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