 |
    |
Locality Preserving Non-negative Basis Learning with Graph Embedding
Yasser Ghanbari1, John Herrington2, Ruben C. Gur3, Robert T. Schultz2, and Ragini Verma1
1Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
Yasser.Ghanbari@uphs.upenn.edu
Ragini.Verma@uphs.upenn.edu
2Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
herringtonj@mail.chop.edu
schultzrt@mail.chop.edu
3Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
gur@mail.med.upenn.edu
Abstract. The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graph-theoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.
Keywords: Connectivity analysis, non-negative matrix factorization, locality-preserving dimensionality reduction, graph embedding
LNCS 7917, p. 316 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013