rkcca {RKUM}

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)