Simulation of the SGL(Structured Graph Learning) algorithm

Reference: A Unified Framework for Structured Graph Learning via Spectral Constraints (Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel Palomar)

1.scripts

SGL.m: the main algorithm \ example.m: a tiny example of a 4*4 laplacian matrix input \ twomoon_example.m: an example applying SGL algorithm to cluster the twomoon dataset(k=2)

2.associated functions

L_op.m: the L operator \ L_adj.m: the L* operator \ Linv.m: compute the inverse of the L operator \ vecLmat.m: return R matrix such that vec(L(w)) = Rw \ w_init.m: initialize an appropriate w \ w_update.m: update w \ U_updte.m: update U \ Alg1.m: update lambda

3.dataset

two_moon.mat: the twomoon dataset for clustering task