rkcm: Robsut Kernel Center Matrix

In RKUM: Robust Kernel Unsupervised Methods

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

Author(s)

Md Ashad Alam <[e-mail]>

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

##Dummy data:

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

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

RKUM documentation built on June 22, 2022, 9:06 a.m.