Principal Component Analysis (PCA)
PCA is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on.
In Amazon SageMaker, PCA operates in two modes, depending on the scenario:

regular: For datasets with sparse data and a moderate number of observations and features.

randomized: For datasets with both a large number of observations and features. This mode uses an approximation algorithm.
PCA uses tabular data.
The rows represent observations you want to embed in a lower dimensional space. The columns represent features that you want to find a reduced approximation for. The algorithm calculates the covariance matrix (or an approximation thereof in a distributed manner), and then performs the singular value decomposition on this summary to produce the principal components.
Topics
Input/Output Interface
For training, PCA expects data provided in the train channel, and optionally supports
a dataset passed to the test dataset, which is scored by the final algorithm. Both
recordIOwrappedprotobuf
and CSV
formats are supported
for training. You can use either File mode or Pipe mode to train models on data that
is
formatted as recordIOwrappedprotobuf
or as
CSV
.
For inference, PCA supports text/csv
, application/json
, and
application/xrecordioprotobuf
. Results are returned in either
application/json
or application/xrecordioprotobuf
format
with a vector of "projections."
For more details on training and inference file formats, see the Principal Component Analysis Sample Notebooks and the .
For more information on input and output file formats, see PCA Response Formats for inference and the Principal Component Analysis Sample Notebooks.
EC2 Instance Recommendation
PCA supports both GPU and CPU computation. Which instance type is most performant depends heavily on the specifics of the input data.
Principal Component Analysis Sample Notebooks
For a sample notebook that shows how to use the Amazon SageMaker Principal Component Analysis algorithm to analyze the images of handwritten digits from zero to nine in the MNIST dataset, see An Introduction to PCA with MNIST. For instructions how to create and access Jupyter notebook instances that you can use to run the example in Amazon SageMaker, see Using Notebook Instances. Once you have created a notebook instance and opened it, select the SageMaker Examples tab to see a list of all the Amazon SageMaker samples. The topic modeling example notebooks using the NTM algorithms are located in the Introduction to Amazon algorithms section. To open a notebook, click on its Use tab and select Create copy.