Making sense of principal component analysis, eigenvectors & eigenvalues
I am currently going through a PCA tutorial. However, I am a bit confused. For PCA, we calculate the covariance matrix and then find the eigen vectors and eigen values of the covariance matrix. These vectors are the principal components.
- How are the eigenvectors and the principal components related?
- Why does finding the covariance matrix from the data and its eigenvectors are the principal components?