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Data Reduction with Principal Component Analysis (PCA)
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:doc:`/SingleStepClassifiers/NonCausalStep/index`

This step applies the *Principal Component Analysis* algorithm to search for orthogonal bases that best represent the data. Using these orthogonal bases, the original data can be projected onto a much smaller space. The size of the original data can be significantly reduced in this way.

.. rubric:: Input Parameters

1. Normalized input data (large size)

.. rubric:: Output Parameters

1. Reduced data set

.. rubric:: Workflow

.. image:: workflow.svg


.. rubric:: Algorithm

:doc:`/Algorithms/PrincipalComponentAnalysis/index`

.. rubric:: References

- J.\  Han, M. Kamber and J. Pei, Data Mining - Concepts and Techniques, 3rd ed., Amsterdam: Morgan Kaufmann Publishers, 2012.