RKUM (version 0.1.1.1)

ifmkcca: Influence Function of Multiple Kernel Canonical Analysis

Description

## To define the robustness in statistics, different approaches have been pro- posed, for example, the minimax approach, the sensitivity curve, the influence function (IF) and the finite sample breakdown point. Due to its simplic- ity, the IF is the most useful approach in statistical machine learning.

Usage

ifmkcca(xx, yy, zz, kernel = "rbfdot", gamma = 1e-05, ncomps = 1, jth=1)

Arguments

xx

a data matrix index by row

yy

a data matrix index by row

zz

a data matrix index by row

kernel

a positive definite kernel

ncomps

the number of canonical vectors

gamma

the hyper-parameters.

jth

the influence function of the jth canonical vector

Value

iflccor

Influence value of the data by multiple kernel canonical correalation

%% \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.

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

See Also

See also as ifcca

Examples

# NOT RUN {
##Dummy data:

X <- matrix(rnorm(500),100); Y <- matrix(rnorm(500),100); Z <- matrix(rnorm(500),100)

ifmkcca(X,Y, Z, "rbfdot",  1e-05,  2, 1)
# }