Unified and contrasting cuts in multiple graphs: application to medical imaging segmentation
Venue
KDD (2015), pp. 617-626
Publication Year
2015
Authors
Chia-Tung Kuo, Xiang Wang, Peter Walker, Owen Carmichael, Jieping Ye, Ian Davidson
BibTeX
Abstract
The analysis of data represented as graphs is common having wide scale applications
from social networks to medical imaging. A popular analysis is to cut the graph so
that the disjoint subgraphs can represent communities (for social network) or
background and foreground cognitive activity (for medical imaging). An emerging
setting is when multiple data sets (graphs) exist which opens up the opportunity
for many new questions. In this paper we study two such questions: i) For a
collection of graphs find a single cut that is good for all the graphs and ii) For
two collections of graphs find a single cut that is good for one collection but
poor for the other. We show that existing formulations of multiview, consensus and
alternative clustering cannot address these questions and instead we provide novel
formulations in the spectral clustering framework. We evaluate our approaches on
functional magnetic resonance imaging (fMRI) data to address questions such as:
"What common cognitive network does this group of individuals have?" and "What are
the differences in the cognitive networks for these two groups?" We obtain useful
results without the need for strong domain knowledge.
