Name
Abstract | ||
|---|---|---|
The Subspace Selection Problem (SSP) amounts to selecting t out of n given vectors of dimension m, such that they span a subspace in which a given target b ∈ Rm has a closest possible approximation. This model has numerous applications in e.g. signal compression and statistical regression. It is well known that the problem is NP-hard. Based on elements from a forward and a backward greedy method, we develop a randomized search heuristic, which in some sense resembles variable neighborhood search, for SSP. Through numerical experiments we demonstrate that this approach has good promise, as it produces good results at modest computational cost. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74446-7_12 | SLS |
Keywords | Field | DocType |
greedy method,randomized search heuristic,numerical experiment,subspace selection problem,dimension m,subspace selection,closest possible approximation,good promise,bidirectional greedy heuristic,modest computational cost,variable neighborhood search,good result,dimension reduction,random search,greedy heuristic,computer experiment | Heuristic,Mathematical optimization,Dimensionality reduction,Variable neighborhood search,Subspace topology,Regression analysis,Greedy algorithm,Greedy randomized adaptive search procedure,Mathematics,Signal compression | Conference |
Volume | ISSN | Citations |
4638 | 0302-9743 | 5 |
PageRank | References | Authors |
0.67 | 6 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dag Haugland | 1 | 151 | 15.18 |