# Table 1 JAMMIT optimization algorithm

 1. Let $$\mathfrak{D}=\left\{{D}_1,{D}_2,\cdots, {D}_K\right\}$$ be a MMDS 2. Form pre-processed super-matrix $$D= stack\left(\mathfrak{D}\right)$$. 3. Compute best rank-1 approximation, $$\left({u}_0,{v}_0\right)$$ of D such that $$D\approx {u}_0{v}_0^T$$. 4. Compute ℓ1 -regularization $${u}_1$$ of $${u}_0$$: $${u}_1=\underset{u}{ \arg \min}\left({\left\Vert D-u{v}_0^T\right\Vert}_2^2+\lambda {\left\Vert u\right\Vert}_1\right)$$. 5. Compute $${v}_1={D}^T{u}_1$$ to obtain solution $$\left({u}_1,{v}_1\right)$$. 6. Assign $${u}_0\leftarrow {u}_1$$ and $${v}_0\leftarrow {v}_1$$. 7. Repeat steps 4–6 until convergence to final solution $$\left(u,v\right)$$ where $$v={D}^Tu$$. 8. Form MMSIG $$\zeta$$ composed of variables selected by the non-zero entries of $$u$$. 9. Parse $$\zeta$$ according to stacking order of the $${D}_k$$ in $$D$$ to obtain $${\zeta}_k$$ for each $${D}_k$$. 10. Parse $$u$$ according to stacking order of $${D}_k$$ in $$D$$ to obtain $${u}_k$$ for each $${D}_k$$. 11. Compute sequence of sparse rank-1 approximations $$\widehat{D}=\left\{{\widehat{D}}_1,{\widehat{D}}_2,\cdots, {\widehat{D}}_K\right\}$$ where $${\widehat{D}}_k\approx {u}_k{v}^T$$ for $$k=1,2,\cdots, K$$.