ifrkcca | R Documentation |
##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.
ifrkcca(X, Y, lossfu = "Huber", kernel = "rbfdot", gamma = 0.00001, ncomps = 10, jth = 1)
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 |
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 |
Md Ashad Alam <malam@tulane.edu>
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 as rkcca
, ifrkcca
##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)
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