Data dimensionality reduction
This topic describes the Data Dimensionality Reduction component.
Description
The Data Dimensionality Reduction component reduces the dimensionality of online data using a model generated by the Principal Component Analysis (PCA) component.
Calculation logic
PCA is a statistical method for dimensionality reduction. It transforms original variables into a new set of uncorrelated composite variables. A smaller number of these composite variables can then be selected to represent most of the information from the original variables.
Parameters
IN port - Input parameters
Parameter name | Description | Required | Data type |
Model application | Select the model type and a specific model. Then, configure the model's input data. | Yes | Feature variables: Integer or floating-point number Note If non-numeric data exists, an exception is thrown. |
OUT port - Output parameters
Parameter | Description | Required | Output data type |
OUT | The number of output parameters corresponds to the number of principal components that you configured during model training. The parameters are named `pca_i`, where `i` is an integer that increments from 1 to `n`, and `n` is the number of principal components. For example, if the number of principal components is 3, the component outputs three parameters: `pca_1`, `pca_2`, and `pca_3`. | No | Floating-point number |
Output quality codes
The output quality code is determined as follows:
If the quality codes of all input variables are greater than or equal to 192, the quality code of the output variable is 192.
If the quality code of any input variable is in the range [0, 192), the quality code of the output variable is 0.
If all input variables have a quality code of -1, or if some have a quality code of -1 while the others are greater than or equal to 192, the quality code of the output variable is -1.