Amazon SageMaker
Developer Guide

PCA Hyperparameters

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. For more information about how PCA works, see How PCA Works.

Parameter Name Description
feature_dim

Input dimension.

Required

Valid values: positive integer

mini_batch_size

Number of rows in a mini-batch.

Required

Valid values: positive integer

num_components

The number of principal components to compute.

Required

Valid values: positive integer

algorithm_mode

Mode for computing the principal components.

Optional

Valid values: regular or randomized

Default value: regular

extra_components

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_mean

Indicates whether the data should be unbiased both during training and at inference.

Optional

Valid values: One of true or false

Default value: true