An improved optimization method for the relevance voxel machine

TY - GEN

T1 - An improved optimization method for the relevance voxel machine

AU - Ganz, Melanie

AU - Sabuncu, Mert R.

AU - Van Leemput, Koen

N1 - Conference code: 4

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-02267-3_19

DO - 10.1007/978-3-319-02267-3_19

M3 - Article in proceedings

SN - 978-3-319-02266-6

T3 - Lecture notes in computer science

SP - 147

EP - 154

BT - Machine Learning in Medical Imaging

A2 - Wu, Guorong

A2 - Zhang, Daoqiang

A2 - Shen, Dinggang

A2 - Yan, Pingkun

A2 - Suzuki, Kenji

A2 - Wang, Fei

PB - Springer

T2 - 4th International Workshop on Machine Learning in Medical Imaging

Y2 - 22 September 2013 through 22 September 2013

ER -