Name
Abstract | ||
|---|---|---|
Finding the rank of a tensor is a problem that has many applications. Unfortunately, it is often very difficult to determine the rank of a given tensor. Inspired by the heuristics of convex relaxation, we consider the nuclear norm instead of the rank of a tensor. We determine the nuclear norm of various tensors of interest. Along the way, we also do a systematic study various measures of orthogonality in tensor product spaces and we give a new generalization of the singular value decomposition to higher-order tensors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10208-015-9264-x | Foundations of Computational Mathematics |
Keywords | Field | DocType |
Tensor decomposition,Nuclear norm,Singular value decomposition,PARAFAC,CANDECOMP,15A69,15A18 | Tensor product,Rank (linear algebra),Tensor density,Mathematical optimization,Tensor (intrinsic definition),Mathematical analysis,Cartesian tensor,Symmetric tensor,Tensor product of Hilbert spaces,Tensor contraction,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1308.3860 | 3 | 1615-3375 |
Citations | PageRank | References |
5 | 0.47 | 19 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harm Derksen | 1 | 151 | 15.00 |