# PCA Hyperparameters
<a name="PCA-reference"></a>

In the `CreateTrainingJob` request, you specify the training algorithm. You can also specify algorithm-specific HyperParameters as string-to-string maps. The following table lists the hyperparameters for the PCA training algorithm provided by Amazon SageMaker AI. For more information about how PCA works, see [How PCA Works](how-pca-works.md). 


| Parameter Name | Description | 
| --- | --- | 
| feature\$1dim |  Input dimension. **Required** Valid values: positive integer  | 
| mini\$1batch\$1size |  Number of rows in a mini-batch. **Required** Valid values: positive integer  | 
| num\$1components |  The number of principal components to compute. **Required** Valid values: positive integer  | 
| algorithm\$1mode |  Mode for computing the principal components.  **Optional** Valid values: *regular* or *randomized* Default value: *regular*  | 
| extra\$1components |  As the value increases, the solution becomes more accurate but the runtime and memory consumption increase linearly. The default, -1, means the maximum of 10 and `num_components`. Valid for *randomized* mode only. **Optional** Valid values: Non-negative integer or -1 Default value: -1  | 
| subtract\$1mean |  Indicates whether the data should be unbiased both during training and at inference.  **Optional** Valid values: One of *true* or *false* Default value: *true*  |