An Improved Optimization Method for the Relevance Voxel Machine

  • Melanie Ganz
  • Mert R. Sabuncu
  • Koen Van Leemput
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


In this paper, we will re-visit the Relevance Voxel Machine (RVoxM), a recently developed sparse Bayesian framework used for predicting biological markers, e.g., presence of disease, from high-dimensional image data, e.g., brain MRI volumes. The proposed improvement, called IRVoxM, mitigates the shortcomings of the greedy optimization scheme of the original RVoxM algorithm by exploiting the form of the marginal likelihood function. In addition, it allows voxels to be added and deleted from the model during the optimization. In our experiments we show that IRVoxM outperforms RVoxM on synthetic data, achieving a better training cost and test root mean square error while yielding sparser models. We further evaluated IRVoxM’s performance on real brain MRI scans from the OASIS data set, and observed the same behavior - IRVoxM retains good prediction performance while yielding much sparser models than RVoxM.


Root Mean Square Error Synthetic Data Marginal Likelihood Relevance Vector Machine Sparse Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Duarte, J.V., Ribeiro, M.J., Violante, I.R., Cunha, G., Silva, E., Castelo-Branco, M.: Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1. Human Brain Mapping (2012)Google Scholar
  2. 2.
    Yang, Z., Fang, F., Weng, X.: Recent developments in multivariate pattern analysis for functional mri. Neuroscience Bulletin 28(4), 399–408 (2012)CrossRefGoogle Scholar
  3. 3.
    Tipping, M.: Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1, 211–244 (2001)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  5. 5.
    Tipping, M.E., Faul, A.: Fast marginal likelihood maximisation for sparse bayesian models. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, pp. 3–6 (2003)Google Scholar
  6. 6.
    Sabuncu, M., Van Leemput, K.: The relevance voxel machine (rvoxm): A self-tuning bayesian model for informative image-based prediction. IEEE Transactions on Medical Imaging 31(12), 2290–2306 (2012)CrossRefGoogle Scholar
  7. 7.
    Ganz, M., Sabuncu, M.R., van Leemput, K.: The improved relevance voxel machine. Technical Report DTU Compute-2013-10, Institute for Mathematical Modeling, DTU (2013)Google Scholar
  8. 8.
    Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., Buckner, R.: Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  9. 9.
    Michel, V., Eger, E., Keribin, C., Thirion, B.: Multiclass sparse bayesian regression for fmri-based prediction. Journal of Biomedical Imaging 2011, 2 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Melanie Ganz
    • 1
    • 2
  • Mert R. Sabuncu
    • 1
  • Koen Van Leemput
    • 1
    • 3
    • 4
  1. 1.Martinos Center for Biomedical Imaging, Harvard Medical SchoolMGHUSA
  2. 2.Department for Computer ScienceUniversity of CopenhagenDenmark
  3. 3.Department of Applied Mathematics and Computer ScienceDTUDenmark
  4. 4.Departments of Information and Computer Science and of Biomedical Engineering and Computational ScienceAalto UniversityFinland

Personalised recommendations