ifrkcca: Influence Function of Robust Kernel Canonical Analysis

In RKUM: Robust Kernel Unsupervised Methods

Influence Function of Robust 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

ifrkcca(X, Y, lossfu = "Huber", kernel = "rbfdot", gamma = 0.00001, ncomps = 10, jth = 1)

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
jth	the influence function of the jth canonical vector

Value

ifrkcor	Influence value of the data by robust kernel canonical correalation
ifrkxcv	Influence value of cnonical vector of X dataset
ifrkycv	Influence value of cnonical 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 rkcca, ifrkcca

Examples

##Dummy data:

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

ifrkcca(X,Y, lossfu = "Huber", kernel = "rbfdot", gamma = 0.00001, ncomps = 10, jth = 2)

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