Machine Learning, Artificial Intelligence, Data Science.
eigenvectors (v₁, v₂, … & vn and corresponding eigenvalues λ₁, λ₂, … & λn), we can rearrange into For example, However, there are some serious limitations on eigendecomposition. First, A needs to be a square matrix. Second, V may not be invertible. As a preview, SVD solves both problems and SVD