Name
Abstract | ||
|---|---|---|
We present a theory for Euclidean dimensionality reduction with subgaussian matrices which unifies several restricted isometry property and Johnson–Lindenstrauss-type results obtained earlier for specific datasets. In particular, we recover and, in several cases, improve results for sets of sparse and structured sparse vectors, low-rank matrices and tensors, and smooth manifolds. In addition, we establish a new Johnson–Lindenstrauss embedding for datasets taking the form of an infinite union of subspaces of a Hilbert space. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10208-015-9280-x | Foundations of Computational Mathematics |
Keywords | Field | DocType |
Random dimensionality reduction,Johnson–Lindenstrauss embeddings,Restricted isometry properties,Compressed sensing,Union of subspaces,60F10,68Q87 | Hilbert space,Discrete mathematics,Embedding,Dimensionality reduction,Tensor,Matrix (mathematics),Mathematical analysis,Pure mathematics,Linear subspace,Restricted isometry property,Mathematics,Manifold | Journal |
Volume | Issue | ISSN |
abs/1402.3973 | 5 | 1615-3375 |
Citations | PageRank | References |
4 | 0.43 | 33 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sjoerd Dirksen | 1 | 32 | 2.75 |