Subset selection in sparse matrices
Abstract
In subset selection we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint, subset selection is NP-hard and few classes are known to be solvable in polynomial time. Using mainly tools from discrete geometry, we show that some sparsity conditions on the original data matrix allow us to solve the problem in polynomial time.
- Publication:
-
arXiv e-prints
- Pub Date:
- October 2018
- DOI:
- 10.48550/arXiv.1810.02757
- arXiv:
- arXiv:1810.02757
- Bibcode:
- 2018arXiv181002757D
- Keywords:
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- Mathematics - Optimization and Control;
- Computer Science - Computational Complexity;
- Computer Science - Machine Learning