Oracle Nonnegative Matrix Factorization (NMF)

Nonnegative Matrix Factorization (NMF) is useful for reducing a large dataset into representative attributes. Similar to Principal Components Analysis (PCA) in concept, but able to handle larger amounts of attributes and in an additive representation model, NMF is a powerful, state-of-the-art data mining algorithm that can be used for a variety of use cases.

NMF can be used to reduce large amounts of data, text data for example, into smaller, more sparse representations that reduce the dimensionality of the data (the same information can be preserved using far fewer variables). The output of NMF models can be analyzed using supervised learning techniques such as SVMs or unsupervised learning techniques such as clustering techniques. Oracle Data Mining uses NMF and SVM algorithms to mine unstructured text data.