0

I understand that the idea of principle component analysis is to find the projection onto a vector with the largest variance.

The book says this can be achieved with eigen decomposition of the covariance matrix and Im wondering why that is. It writes that the eigenvector associated with the eigenvalue with the largest eigenvalue will have the highest variance when the matrix is projected on it. Then it doesn't go into why this is so. What is the intuition?

Qwertford
  • 835
  • Have a look at this answer: https://math.stackexchange.com/questions/243533/how-to-intuitively-understand-eigenvalue-and-eigenvector – StatsyBanksy Mar 27 '19 at 09:09
  • https://math.stackexchange.com/questions/23596/why-is-the-eigenvector-of-a-covariance-matrix-equal-to-a-principal-component/29230#29230 might help too – Henry Mar 27 '19 at 09:58

0 Answers0