Recently, I have come across a paper which has used a unique way of augmenting the data. If the data has multiple channels say we have a $x_i , i=1...N$ as a column feature vector. If we compute the the eigenvalues and eigenvector of the whole samples $x$, the following is a method of augmenting the data to increase the number of samples:
In which $\alpha$ is a random number, usually from a normal distribution, $\lambda_i $ is a eigenvalue and $p_i$ is a eigenvector.
I couldn't find any sources for this. Can anyone elaborate?