KPCA_model: kernel PCA for process monitor

In chengfeifan/monitor: Write Process monitoring method

Description

Use kernel principle component analysis for process monitoring and also find the squared prediction error (SPE) and Hotelling's T2 test statistic values for each observation in this data matrix.

Usage

KPCA_model(data, kernel, kernel_num, ...)

Arguments

data	A centered-and-scaled data matrix
kernel	The kernel function to be used to calculate the kernel matrix. This has to be a function of class kernel, i.e. which can be generated either one of the build in kernel generating functions (e.g., rbfdot etc.) or a user defined function of class kernel taking two vector arguments and returning a scalar.
kernel_num	The number of principle component
...	Lazy dots for additional internal arguments

Value

A list of class 'KPCA' with following

• projectionMatrix – the q eigenvectors corresponding to the q largest eigenvalues as a p x q projection matrix

• LambdaInv – the diagonal matrix of inverse eigenvalues

• SPE – the vector of SPE test statistic values for each of the n observations contained in "data"

• T2 – the vector of Hotelling's T2 test statistic for each of the same n observations

• model_data – the data used to build the model

• K_hat – The mean and center matrix of kernel matrix

• eigenK – A list consists of eigen values and eigen vector of K_hat

References

http://modeleau.fsg.ulaval.ca/fileadmin/modeleau/documents/Publications/pvr487.pdf

chengfeifan/monitor documentation built on May 14, 2019, 2:29 p.m.