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Reducing Sampling Ratios Improves Bagging in Sparse Regression
With a properly chosen sampling ratio, a reasonably small number of estimates K = 30 gives satisfying result, even though increasing K is discovered to always improve or at least maintain the performance.
JOBS: Joint-Sparse Optimization from Bootstrap Samples
In practice, it is often the case that not all measurements are available or required for recovery.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
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