RKUM (version 0.1.1.1)

rkcm: Robsut Kernel Center Matrix

Description

# A functioin

Usage

rkcm(X, lossfu = "Huber", kernel = "rbfdot")

Arguments

X

a data matrix index by row

lossfu

a loss function: square, Hampel's or Huber's loss

kernel

a positive definite kernel

Value

rkcm

a square robust kernel center matrix

%% \item{comp2 }{Description of 'comp2'} %% ...

References

Md Ashad Alam, Kenji Fukumizu and Yu-Ping Wang (2018), Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Vol. 304 (2018) 12-29.

Md Ashad Alam, Vince D. Calhoun and Yu-Ping Wang (2018), Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics, Computational Statistics and Data Analysis, Vol. 125, 70- 85

See Also

See also as ifcca, rkcca, ifrkcca

Examples

# NOT RUN {
##Dummy data:

X <- matrix(rnorm(2000),200); Y <- matrix(rnorm(2000),200)

rkcm(X, "Huber","rbfdot")
# }