rkcca: Robust kernel canonical correlation analysis

In RKUM: Robust Kernel Unsupervised Methods

Robust kernel canonical correlation analysis

Description

#A robust correlation

Usage

rkcca(X, Y, lossfu = "Huber", kernel = "rbfdot", gamma = 1e-05, ncomps = 10)

Arguments

X	a data matrix index by row
Y	a data matrix index by row
lossfu	a loss function: square, Hampel's or Huber's loss
kernel	a positive definite kernel
gamma	the hyper-parameters
ncomps	the number of canonical vectors

Value

An S3 object containing the following slots:

rkcor	Robsut kernel canonical correlation
rxcoef	Robsut kernel canonical coficient of X dataset
rycoef	Robsut kernel canonical coficient of Y dataset
rxcv	Robsut kernel canonical vector of X dataset
rycv	Robsut kernel canonical vector of Y dataset

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.

M. Romanazzi (1992), Influence in canonical correlation analysis, Psychometrika vol 57(2) (1992) 237-259.

See Also

See also as ifcca, rkcca, ifrkcca

Examples

##Dummy data:

X <- matrix(rnorm(1000),100); Y <- matrix(rnorm(1000),100)

rkcca(X,Y, "Huber",  "rbfdot", 1e-05,  10)

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