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Computes the canonical correlation analysis in feature space. Kernel Canonical Correlation Analysis (KCCA) is a non-linear extension of CCA. Given two random variables (or datasets), KCCA aims at extracting the information which is shared by the two random variables (or datasets). more information found at kernlab::kcca()

Usage

kcca_correlate(x, y = NULL, kernel = "rbfdot", gamma = 0.1, num_comp = 10, ...)

Arguments

x

variable or dataframe

y

variable or dataframe

kernel

a kernel to use

gamma

regularization parameter

num_comp

number of components

...

pass through args for kcca function

Value

A kernel canonical correlation analysis data frame kcor_df