Supervised Block Sparse Dictionary Learning for Simultaneous Clustering and Classification in Computational Anatomy

  • Erdem Varol
  • Christos Davatzikos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects.


Autism Spectrum Disorder Multiple Instance Dictionary Learning Geometric Programming Multiple Instance Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Erdem Varol
    • 1
  • Christos Davatzikos
    • 1
  1. 1.University of PennsylvaniaUSA

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