Table 1.

Major mathematical notations used in this paper.

Mathematical notation	Definition
${\mathcal{T}}^s$	brain tensor of subject s in ${ℝ}^{n_r\times n_r\times n_v}$
Xk	brain network in ${ℝ}^{n_r\times n_r}$ denoting the k-th frontal-view of tensor $\mathcal{T}$
${\text{y}}_{ij}^s$	vector of subject s including connectivity weights between the i-th and j-th ROIs across all views
Hs	high-order morphological brain network for subject s
hs	high-order feature vector extracted from the upper triangular part of Hs
Kl	l-th learning kernel in ℝn×n
n	number of subjects
S	similarity matrix in ℝn×n for connectomic manifold learning
L	latent matrix in ℝn×c
c	number of clusters
m	number of kernels
w	weighting vector of the kernels in ℝm
In	identity matrix in ℝn×n