Better than best low-rank approximation with the singular value
decomposition
Better than best low-rank approximation with the singular value
decomposition
The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on the organization of the matrix data. In particular, we will show examples …