Table I. Brain functional network construction models.

Method	Data-fitting term	Regularization term
PC	${\Vert \mathrm{W}-X^{T}X\Vert }_{\mathrm{F}}^2$	—
SR	$\frac{1}{2}{\Vert X-XW\Vert }_{\mathrm{F}}^2$	λ‖W‖1
WSR	$\frac{1}{2}{\Vert X-XW\Vert }_{\mathrm{F}}^2$	λ‖C ⊙ W‖1
SGR	$\frac{1}{2}{\Vert X-XW\Vert }_{\mathrm{F}}^2$	${\lambda }_1{\Vert W\Vert }_1+{\lambda }_2{{\sum }_{k=1}^K{d}_k\Vert W_{G_k}\Vert }_q$
WSGR	$\frac{1}{2}{\Vert X-XW\Vert }_{\mathrm{F}}^2$	${\lambda }_1{\Vert C\odot W\Vert }_1+{\lambda }_2{{\sum }_{k=1}^K{d}_k\Vert W_{G_k}\Vert }_q$

Note: The regularized parameters λ, λ₁, λ₂ are positive; W_ii =0, ∀ i=1, 2,…, N.

PC, Pearson's correlation; SR, sparse representation; SGR, sparse group representation; WSR, weighted sparse representation; WSGR, weighted sparse group representation.