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Saintigny et al. [ 3] conducted a study on the same dataset using a boosting approach which is also a sparse technique.
Thus, a more efficient sparse technique is required, which could generate robust brain parcellation by guaranteeing the stability of parcellation and retaining the individual variability at the same time.
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In this paper we present fastsg, an optimized library for the sparse grid technique with support for dimensional truncation.
We also show that though our multi-regularized sparse reconstruction technique improves over conventional sparse recovery, it still suffers from the graph-signal smoothness assumption it implicitly considers.
The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem.
We investigate the underpinnings of the sparse reconstruction technique via simulations that reveal its characteristics and how they affect its performance.
The numerical results indicate that the algorithms based on a preconditioned sparse matrix technique and on a specially designed algorithm for the particular problem under consideration perform best.
We perform the first application of the Elastic Net sparse regression technique to fMRI data and find that reliability can be enhanced independently of prediction, particularly by accounting for the known spatial structure in the data.
We conduct experiments using the Nanyang Technological University (NTU) campus landmark database to show that the proposed method achieves a high recognition rate than ELM and a lower response time than the sparse representation technique.
More recently, Kerse et al. proposed a method for direct target localization based on a standard sparse identification technique [18].
One drawback of the sparse representation technique is the computational complexity that is related to the dictionary size.
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