They're not missing just judiciously omitted. Actually seeing SVD presented in an introduction is what inspired me to post this.
The SVD approach works for an matrix A in R^{m x n}
A = UΣV^T
But if A is square and you allow the singular values to be +/-, then you can find a single matrix U that serves as a left-basis and a right-basis for A. Hence we obtain the eigendecomposition:
A = UΛU^T
So SVD is the mother ship, and eigenstuff is a special case.
They're not missing just judiciously omitted. Actually seeing SVD presented in an introduction is what inspired me to post this.
The SVD approach works for an matrix A in R^{m x n}
But if A is square and you allow the singular values to be +/-, then you can find a single matrix U that serves as a left-basis and a right-basis for A. Hence we obtain the eigendecomposition: So SVD is the mother ship, and eigenstuff is a special case.