Name
Abstract | ||
|---|---|---|
Let X be a data matrix of rank ρ, representing n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability , the margin and minimum enclosing ball in the feature space are preserved to within-relative error, ensuring comparable generalization as in the original space. We present extensive experiments with real and synthetic data to support our theory. |
Year | Venue | Keywords |
|---|---|---|
2012 | AISTATS | Dimensionality reduction,random projection,Support Vector Machines |
DocType | Volume | Citations |
Journal | abs/1211.6085 | 25 |
PageRank | References | Authors |
0.92 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saurabh Paul | 1 | 54 | 3.23 |
| Christos Boutsidis | 2 | 610 | 33.37 |
| Malik Magdon-Ismail | 3 | 914 | 104.34 |
| Petros Drineas | 4 | 2165 | 201.55 |