KernelPrincipalComponentAnalysis Properties

Gets or sets a boolean value indicating whether this analysis should store enough information to allow the reversion of the transformation to be computed. Set this to no in case you would like to store the analysis object to disk and you do not need to reverse a transformation after it has been computed.

Gets or sets whether the points should be centered in feature space.

Gets a matrix whose columns contain the principal components. Also known as the Eigenvectors or loadings matrix.

The respective role each component plays in the data set.

Gets the Principal Components in a object-oriented structure.

Gets a matrix whose columns contain the principal components. Also known as the Eigenvectors or loadings matrix.

The cumulative distribution of the components proportion role. Also known as the cumulative energy of the principal components.

Provides access to the Eigenvalues stored during the analysis.

Gets or sets the amount of explained variance that should be generated by this model. This value will alter the NumberOfOutputs that can be generated by this model.

Gets or sets the Kernel used in the analysis.

Gets the maximum number of outputs (dimensionality of the output vectors) that can be generated by this model.

Gets the column mean of the source data given at method construction.

Gets or sets the method used by this analysis.

Gets the number of inputs accepted by the model.

Gets or sets the number of outputs (dimensionality of the output vectors) that should be generated by this model.

Gets or sets whether calculations will be performed overwriting data in the original source matrix, using less memory.

Gets the resulting projection of the source data given on the creation of the analysis into the space spawned by principal components.

Provides access to the Singular Values stored during the analysis. If a covariance method is chosen, then it will contain an empty vector.

Returns the original data supplied to the analysis.

Gets the column standard deviations of the source data given at method construction.

Gets or sets the minimum variance proportion needed to keep a principal component. If set to zero, all components will be kept. Default is 0.001 (all components which contribute less than 0.001 to the variance in the data will be discarded).

Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.

Gets or sets whether the transformation result should be whitened (have unit standard deviation) before it is returned.