Name
Abstract | ||
|---|---|---|
This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, multivariate arrays for which the entries change sign when permuting pairs of indices. We show which ranks can be attained by an antisymmetric tensor and discuss the adaption of existing approximation algorithms to preserve antisymmetry, most notably a Jacobi algorithm. Particular attention is paid to the important special case when choosing the rank equal to the order of the tensor. It is shown that this case can be addressed with an unstructured rank-1 approximation. This allows for the straightforward application of the higher-order power method, for which we discuss effective initialization strategies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1137/16M106618X | SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS |
Keywords | Field | DocType |
tensor,antisymmetric,low rank,singular value decomposition,Jacobi rotation | Singular value decomposition,Approximation algorithm,Jacobi rotation,Antisymmetric tensor,Tensor,Mathematical analysis,Jacobi eigenvalue algorithm,Antisymmetric relation,Multilinear map,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 3 | 0895-4798 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erna Begovic Kovac | 1 | 1 | 0.36 |
| Daniel Kressner | 2 | 449 | 48.01 |