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Oracle® Data Mining Concepts
11g Release 1 (11.1)

Part Number B28129-01
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16 Non-Negative Matrix Factorization

This chapter describes Non-Negative Matrix Factorization, the unsupervised algorithm used by Oracle Data Mining for feature extraction.

See Also:

Chapter 9, "Feature Selection and Extraction"

This chapter contains the following topics:

About NMF

Non-Negative Matrix Factorization (NMF) is a feature extraction algorithm that decomposes multivariate data by creating a user-defined number of features, which results in a reduced representation of the original data.


Non-Negative Matrix Factorization (NMF) is described in the paper "Learning the Parts of Objects by Non-Negative Matrix Factorization" by D. D. Lee and H. S. Seung in Nature (401, pages 788-791, 1999).

NMF decomposes a data matrix V into the product of two lower rank matrices W and H so that V is approximately equal to W times H. NMF uses an iterative procedure to modify the initial values of W and H so that the product approaches V. The procedure terminates when the approximation error converges or the specified number of iterations is reached.

Each feature is a linear combination of the original attribute set; the coefficients of these linear combinations are non-negative.

During model apply, an NMF model maps the original data into the new set of attributes (features) discovered by the model.

NMF for Text Mining

Text mining involves extracting information from unstructured data. Typically, text data is high-dimensional and sparse. Unsupervised algorithms like Principal Components Analysis (PCA), Singular Value Decomposition (SVD), and NMF involve factoring the document-term matrix based on different constraints. One widely used approach for text mining is latent semantic analysis.

NMF focuses on reducing dimensionality. By comparing the vectors for two adjoining segments of text in a high-dimensional semantic space, NMF provides a characterization of the degree of semantic relatedness between the segments. NMF is less complex than PCA and can be applied to sparse data. NMF-based latent semantic analysis is an attractive alternative to SVD approaches due to the additive non-negative nature of the solution and the reduced computational complexity and resource requirements.

Data Preparation for NMF

The presence of outliers can significantly impact NMF models. Use a clipping transformation before you bin or normalize the table to avoid the problems caused by outliers for these algorithms.

NMF may benefit from normalization.

Outliers with min-max normalization cause poor matrix factorization. To improve the matrix factorization, you need to decrease the error tolerance. This in turn leads to longer build times.