Corpus ID: 14454219

10, 000+ Times Accelerated Robust Subset Selection (ARSS)

@article{Zhu2014100T,
  title={10, 000+ Times Accelerated Robust Subset Selection (ARSS)},
  author={Feiyun Zhu and Bin Fan},
  journal={ArXiv},
  year={2014},
  volume={abs/1409.3660}
}
Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Specifically in the subset selection area, this is the first attempt to employ the lp (0 < p ≤ 1)-norm based measure for the representation loss, preventing large errors from… Expand
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References

SHOWING 1-10 OF 30 REFERENCES
Early Active Learning via Robust Representation and Structured Sparsity
See all by looking at a few: Sparse modeling for finding representative objects
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization
Robust Matrix Completion via Joint Schatten p-Norm and lp-Norm Minimization
Active learning via transductive experimental design
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity
Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery
New primal SVM solver with linear computational cost for big data classifications
...
1
2
3
...