## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# Principal Components Analysis Transform

The PCA transform algorithm transforms the data set to principal components.

## Details

Given a transformation matrix computed by PCA (eigenvectors in row-major order) and data set as input, the PCA Transform algorithm transforms input data set of size to the data set of size , .

## Batch Processing

Algorithm Input
The PCA Transform algorithm accepts the input described below. Pass the
`Input ID`
as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Algorithm Input for Principal Components Analysis Transform (Batch Processing)
Input ID
Input
data
Use when the input data is a normalized or non-normalized data set.
Pointer to the numeric table that contains the input data set. This input can be an object of any class derived from
NumericTable
.
eigenvectors
Principal components computed using the PCA algorithm.
Pointer to the numeric table . You can define it as an object of any class derived from
NumericTable
, except for
PackedSymmetricMatrix
,
PackedTriangularMatrix
, and
CSRNumericTable
.
dataForTransform
Optional. Pointer to the key value-data collection containing the following data for PCA. The collection contains the following key-value pairs:
mean
means
variance
variances
eigenvalue
eigenvalues
• If you do not provide the collection, the library will not apply the corresponding centering, normalization or whitening operation.
• If one of the numeric tables in collection is
NULL
, the corresponding operation will not be applied: centering for means, normalization for variances, whitening for eigenvalues.
• If mean or variance tables exist, it should be a pointer to the numeric table.
• If eigenvalue table is not
NULL
, it is the pointer to () numeric table, where the number of the columns is greater than or equal to
nComponents
.
Algorithm Parameters
The PCA Transform algorithm has the following parameters:
Algorithm Parameters for Principal Components Analysis Transform (Batch Processing)
Parameter
method
Default Value
Description
algorithmFPType
defaultDense
or
svdDense
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
nComponents
defaultDense
The number of principal components . If zero, the algorithm will compute the result for .
Algorithm Output
The PCA Transform algorithm calculates the results described below. Pass the
Result ID
as a parameter to the methods that access the results of your algorithm.
Algorithm Output for Principal Components Analysis Transform (Batch Processing)
Result ID
Result
transformedData
Pointer to the numeric table that contains data projected to the principal components basis.
By default, this result is an object of the
HomogenNumericTable
class, but you can define the result as an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
,
PackedTriangularMatrix
, and
CSRNumericTable
.

## Examples

C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python* with DPC++ support
Batch Processing:
Python*
Batch Processing:

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.