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The SVD algorithm supports the following settings:
Approximate Computation: Specifies whether the algorithm should use approximate computations to improve performance. For SVD, approximation is often appropriate for data sets with many columns. An approximate low-rank decomposition provides good solutions at a reasonable computational cost. If you disable approximate computations for SVD, then approximation depends on the characteristics of the data. For data sets with more than 2500 attributes (the maximum number of features allowed) only approximate decomposition is possible. If approximate computation is disabled for a data set with more than 2500 attributes, then an exception is raised.
Values for Approximate Computation are
System Determined (Default)
Enable
Disable
Automatic Preparation: ON or OFF. The default is ON.
Number of Features: System Determined (Default). You can specify a number.
Scoring Mode: It is the scoring mode to use, either Singular Value Decomposition Scoring or Principal Components Analysis Scoring. The default is Singular Value Decomposition Scoring (SVD scoring).
When the build data is scored with SVD, the projections will be the same as the U matrix.
When the build data is scored with PCA, the projections will be the product of the U and S matrices.
U Matrix Output Whether or not the U matrix produced by SVD persists. The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix persists only when U Matrix Output is enabled. When U Matrix Output is enabled, the build data must include a Case ID. The default is Disable.