rkcm | R Documentation |
# A functioin
rkcm(X, lossfu = "Huber", kernel = "rbfdot")
X |
a data matrix index by row |
lossfu |
a loss function: square, Hampel's or Huber's loss |
kernel |
a positive definite kernel |
rkcm |
a square robust kernel center matrix |
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.
Md Ashad Alam, Vince D. Calhoun and Yu-Ping Wang (2018), Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics, Computational Statistics and Data Analysis, Vol. 125, 70- 85
See also as ifcca
, rkcca
, ifrkcca
##Dummy data: X <- matrix(rnorm(2000),200); Y <- matrix(rnorm(2000),200) rkcm(X, "Huber","rbfdot")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.