KPCA VI

Owning Palette: Feature Manipulation VIs

Requires: Analytics and Machine Learning Toolkit

Trains a kernel principal component analysis (KPCA) model. You can use the KPCA model to reduce the dimension of training data.

Example

	model in specifies the information about the entire workflow of the model.
	PCA settings specifies the method and value for this VI to calculate the number of principal components. criteria specifies the method for this VI to calculate the number of principal components. 0 number of components (default)—Directly specifies the number of principal components. 1 ratio of variance—Specifies the ratio of variance for this VI to calculate the number of principal components. number of components specifies the number of principal components. This input is valid only if criteria is number of components. ratio of variance specifies the ratio of the variance in principal components to the total variance. This VI automatically calculates the number of components. The default is 0.95. This input is valid only if criteria is ratio of variance.
	kernel settings specifies the settings to configure the kernel function. This VI supports the following kernel functions: Kernel function Definition Description Polynomial function x and y are the input sample vectors; gamma, coef0, and degree are the algorithm coefficients. Radial basis function x and y are the input sample vectors. Sigmoid function x and y are the input sample vectors; gamma and coef0 are the algorithm coefficients. type specifies type of the kernel function. 0 Polynomial (default)—Polynomial function 1 RBF—Radial basis function 2 Sigmoid—Sigmoid function degree specifies the degree coefficient of the kernel function. The default is 3. gamma specifies the gamma coefficient of the kernel function. The default is 1. coef0 specifies the coef0 coefficient of the kernel function. The default is 0.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	model out returns the information about the entire workflow of the model. Wire model out to the reference input of a standard Property Node to get an AML Analytics Property Node.
	KPCA model info returns the information of the KPCA model. Wire KPCA model info to the reference input of a standard Property Node to get an AML KPCA Property Node.
	error out contains error information. This output provides standard error out functionality.

Example

Refer to the Feature Manipulation (Training) VI in the labview\examples\AML\Feature Manipulation directory for an example of using the KPCA VI.