Locality Preserving Non-negative Basis Learning with Graph Embedding

  • Yasser Ghanbari
  • John Herrington
  • Ruben C. Gur
  • Robert T. Schultz
  • Ragini Verma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


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.


Connectivity analysis non-negative matrix factorization locality-preserving dimensionality reduction graph embedding 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yasser Ghanbari
    • 1
  • John Herrington
    • 2
  • Ruben C. Gur
    • 3
  • Robert T. Schultz
    • 2
  • Ragini Verma
    • 1
  1. 1.Section of Biomedical Image AnalysisUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Center for Autism ResearchChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  3. 3.Brain Behavior Laboratory, Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaUSA

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