Can correlation matrix be used as features in machine learning classification Can I use correlation between the training data as features, and if possible how will I test the test data with the model coefficients
I will try to explain more
If the training data are
X = [X1, X2,....., Xn]
and Xi = [Xi1,Xi2,....., Xi100]

where X are the training data and Xi is one sample of the data
and
K = [Xcorr1,1 ... Xcorr1,n
     ...      ...   ...
     Xcorrn,1 ... Xcorrn,n]

K will be something like that
K = [1 .2 .3 .4
     .2 1 .5 .6
     .3 .5 1 .7
     .4 .6 .7 1]

Can I will use this K to train my model ?
If it is possible How will I test my test data which will be
X = [X1, X2,....., Xn]


I don't see it, unless you have something like multiple multivariate time series and take the correlation matrix of the univariate time series (think multiple speech signals that you want to classify as being English or Spanish). (Let's ignore the time dependence and say that we're just going to slug through the calculation.)
In such a situation, that could be a feature extraction method. For each time series, you reduce the time series to a matrix, and then you use the matrix entries to predict the outcome. This even could result in different-length time series having the same number of features. I have my doubts that this will preserve the valuable information needed to make accurate predictions, but maybe there could be situations where this works.
However, if you just have a regular data frame (such as all of the time series concatenated together) and take the covariance matrix, you will wind up with one correlation matrix and a bunch of outcomes.
set.seed(2022)
N <- 1000
p <- 10
X <- matrix(rnorm(N*p, 0, 1), N, p)
y <- X %*% rnorm(p, 0, 1) + rnorm(N, 0, 1)
features <- corr(X)
L <- lm(y ~ features)

You don't have feature values corresponding to each outcome, so this code fails.
