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<doc>

<members>

<member name="PCA">

<summary>

PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace.

</summary>

<remarks>

<a href='https://en.wikipedia.org/wiki/Principal_component_analysis'>Principle Component Analysis (PCA)</a> is a dimensionality-reduction algorithm which computes the projection of the feature vector to onto a low-rank subspace.

Its training is done using the technique described in the paper: <a href='https://arxiv.org/pdf/1310.6304v2.pdf'>Combining Structured and Unstructured Randomness in Large Scale PCA</a>,

and the paper <a href='https://arxiv.org/pdf/0909.4061v2.pdf'>Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>

<para>For more information, see also:</para>

<list type='bullet'>

<item><description>

<a href='https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf'>Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices</a>

</description></item>

<item><description>

<a href='https://arxiv.org/abs/0809.2274'>A randomized algorithm for principal component analysis</a>

</description></item>

<item><description>

<a href='http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-SIREV.pdf'>Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions</a>

</description></item>

</list>

</remarks>

</member>

<example name='PcaCalculator'>

<example>

An example of how to add the PcaCalculator transform to a pipeline with a column named &quot;Features&quot;.

<code language="csharp">

string[] features = new string[&quot;Sepal length&quot;, &quot;Sepal width&quot;, &quot;Petal length&quot;, &quot;Petal width&quot;];

pipeline.Add(new PcaCalculator(columns){ Rank = 3 });

</code>

</example>

</example>

<example name='PcaAnomalyDetector'>

<example>

<code language="csharp">

new PcaAnomalyDetector

{

Rank = 40,

Oversampling = 40,

NormalizeFeatures = Microsoft.ML.Models.NormalizeOption.Warn

}

</code>

</example>

</example>

</members>

</doc>