Name
Abstract | ||
|---|---|---|
We study the (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low-order polynomial-time algorithms to construct such coresets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIT.2013.2272457 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
regression analysis | Combinatorics,Nonparametric regression,Polynomial regression,Nonlinear regression,Local regression,Robust regression,Generalized least squares,Total least squares,Mathematics,Coreset | Journal |
Volume | Issue | ISSN |
59 | 10 | 0018-9448 |
Citations | PageRank | References |
12 | 0.83 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Boutsidis | 1 | 610 | 33.37 |
| Petros Drineas | 2 | 2165 | 201.55 |
| Malik Magdon-Ismail | 3 | 914 | 104.34 |