Name
Abstract | ||
|---|---|---|
Canonical polyadic (CP) tensor decomposition is an important task in many applications. Many times, the true tensor rank is not known, or noise is present, and in such situations, different existing CP decomposition algorithms provide very different results. In this letter, we introduce a notion of sensitivity of CP decomposition and suggest to use it as a side criterion (besides the fitting error... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2943060 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Sensitivity,Matrix decomposition,Signal processing algorithms,Convergence,Symmetric matrices,Optimization | Convergence (routing),Applied mathematics,Pattern recognition,Tensor,Tensor rank,Convolutional neural network,Matrix decomposition,Symmetric matrix,Artificial intelligence,Mathematics,Decomposition,Tensor decomposition | Journal |
Volume | Issue | ISSN |
26 | 11 | 1070-9908 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petr Tichavský | 1 | 341 | 41.01 |
| Anh Huy Phan | 2 | 828 | 51.60 |
| Andrzej Cichocki | 3 | 5228 | 508.42 |