Grassmann manifold and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks' connectivity patterns (FCP).
We selected 82 TRD patients and 41 healthy controls for the data set to complete the multivariate data analysis on the Grassmann manifold and step-wise forward component selection using the support vector machines to obtain the FNs.
Discriminant analysis of functional connectivity patterns on Grassmann manifold.
n,k] is the universal double covering space for the Grassmann manifold [G.
2]) that each oriented Grassmann manifold is, thanks to the fixed point free involution D [right arrow] -- D (where the oriented vector subspace -D is obtained from D by reversing its orientation), unorientedly cobordant to zero.
Work on the cup-length of the oriented Grassmann manifolds has also been done independently by T.
Stiefel-Whitney characteristic classes and parallelizability of Grassmann manifolds, Rend.
On second Stiefel-Whitney class of Grassmann manifolds and cuplength, J.