ifcca: Influence Funciton of Canonical Correlation Analysis

In RKUM: Robust Kernel Unsupervised Methods

Influence Funciton of Canonical Correlation 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

ifcca(X, Y, gamma = 1e-05, ncomps = 2, jth = 1)

Arguments

X	a data matrix index by row
Y	a data matrix index by row
gamma	the hyper-parameters
ncomps	the number of canonical vectors
jth	the influence function of the jth canonical vector

Value

iflccor

Influence value of the data by linear canonical correalation

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)

ifcca(X,Y,  1e-05,  2, 2)

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