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Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are unsupervised algorithms used by Oracle Data Mining for feature extraction.
Unlike Nonnegative Matrix Factorization, SVD and PCA are orthogonal linear transformations that are optimal for capturing the underlying variance of the data. This property is extremely useful for reducing the dimensionality of high-dimensional data and for supporting meaningful data visualization.
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Note: Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) require Oracle Database 12c. |
In addition to dimensionality reduction, SVD and PCA have several other important applications, such as, data denoising (smoothing), data compression, matrix inversion, and solving a system of linear equations. All these areas can be effectively supported by the Oracle Data Mining implementation SVD/PCA.
SVD is implemented as a feature extraction algorithm. PCA is implemented as a special scoring method for the SVD algorithm.
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See Also: For more information about SVD and PCA models and algorithm settings: |