Performs Principal Component Analysis on the supplied dataset

pca.compute(data)
pca.compute(..., 'OptionName', optionValue, ...)

Input

• data input samples stored as the matrix rows or as the matrix columns

Options

• DataAs Data layout option. One of 'Row' or 'Col'. default 'Row':
  • Row indicates that the input samples are stored as matrix rows.
  • Col indicates that the input samples are stored as matrix columns.
• MaxComponents Maximum number of components that PCA should retain. default 0 (all the components are retained).
• RetainedVariance Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2. default 1.0 (all the components are retained).
• Mean Optional mean value. By default, the mean is computed from the data. Not set by default.

Note: RetainedVariance and MaxComponents are mutually exclusive options, and shoudn't be used together.

The method performs PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new eigenvalues, eigenvectors, and mean are allocated and computed.

The computed eigenvalues are sorted from the largest to the smallest and the corresponding eigenvectors are stored as cv.PCA.eigenvectors rows.