Algorithm 5.

Approximating the entropy via random projection matrices

1:	INPUT: Integer n (dimensions of matrix R) and integer k (with rank of R at most k ⪡ n, see eqn. (2)).
2:	Construct the random projection matrix $\mathbf{\Pi }\in {\mathbb{R}}^{n\times s}$ (see Section V-B for details on Π and s).
3:	Compute $\tilde{\mathbf{R}}=\mathbf{R}\mathbf{\Pi }\in {\mathbb{R}}^{n\times s}$.
4:	Compute and return the (at most) k non-zero singular values of $\tilde{\mathbf{R}}$, denoted by ${\tilde{p}}_i$, i = 1 … k.
5:	OUTPUT: ${\tilde{p}}_i$, i = 1 … k and $\widehat{\mathcal{H}}(\mathbf{R})={\sum }_{i=1}^k{\tilde{p}}_i\ln \frac{1}{{\tilde{p}}_i}$.