Name
Abstract | ||
|---|---|---|
Combining information present in multiple datasets is one of the key challenges to fully benefit from the increasing availability of data in a variety of fields. Coupled tensor factorization aims to address this challenge by performing a simultaneous decomposition of different tensors. However, tensor factorization tends to suffer from a lack of robustness as the number of components affects the results to a large extent. In this work, a general framework for coupled tensor factorization is built to extract reliable components. Results from both individual and coupled decompositions are compared and divergence measures are used to adapt the number of components. It results in a joint decomposition method with (i) a variable number of components, (ii) shared and unshared components among tensors and (iii) robust components. Results on simulated data show a better modelling of the sources composing the datasets and an improved evaluation of the number of shared sources. |
Year | Venue | Field |
|---|---|---|
2016 | European Signal Processing Conference | Convergence (routing),Divergence,Tensor,Matrix decomposition,Algorithm,Decomposition method (constraint satisfaction),Robustness (computer science),Theoretical computer science,Stress (mechanics),Mathematics,Tensor decomposition |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthieu Genicot | 1 | 0 | 0.34 |
| Pierre-Antoine Absil | 2 | 348 | 34.17 |
| Renaud Lambiotte | 3 | 920 | 64.98 |
| Saber A Sami | 4 | 1 | 0.70 |