Sparse Decomposition and Modeling of Anatomical Shape Variation

@article{Sjstrand2007SparseDA,
  title={Sparse Decomposition and Modeling of Anatomical Shape Variation},
  author={Karl Sj{\"o}strand and Egill Rostrup and C. Ryberg and Rasmus Larsen and Colin Studholme and H. Baezner and J. Ferro and Franz Fazekas and Leonardo Pantoni and Domenico Inzitari and Gunhild Waldemar},
  journal={IEEE Transactions on Medical Imaging},
  year={2007},
  volume={26},
  pages={1625-1635}
}
Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the… CONTINUE READING
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