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Oracle Tips by Burleson |
Oracle 10g Non-Negative Matrix Factorization
There are two problems for which feature
extractions are useful:
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Extract features from actual text. - Oracle
Text is designed to solve this kind of problem.
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Extract semantic features or higher-level
features from the basic features uncovered when features are
extracted from actual text.
Non-Negative Matrix Factorization (NMF) is a
feature extraction algorithm introduced in Oracle 10g that is used
to extract semantic features. NMF decomposes the data as a product
of two matrices having only non-negative elements. This results in
reduced representation of the original data.
In the reduced data set, each feature is a
linear combination of the original attribute set. The NMF has low
computational cost and the ability to deal with both dense and
sparse data sets. The feature extraction can be used in supporting
the NMF model in Oracle Data Mining and text mining.
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