Name
Title | ||
|---|---|---|
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition. |
Abstract | ||
|---|---|---|
Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/5608286 | COMPLEXITY |
Field | DocType | Volume |
Proper generalized decomposition,Data-driven,Sparse model,Algorithm,Curse of dimensionality,Artificial intelligence,High dimensional space,Machine learning,Mathematics | Journal | 2018 |
ISSN | Citations | PageRank |
1076-2787 | 0 | 0.34 |
References | Authors | |
2 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rubén Ibáñez | 1 | 0 | 0.34 |
| Emmanuelle Abisset-Chavanne | 2 | 2 | 2.24 |
| A. Ammar | 3 | 17 | 6.79 |
| David Martínez González | 4 | 114 | 11.19 |
| E Cueto | 5 | 35 | 6.79 |
| A. Huerta | 6 | 5 | 3.76 |
| Jean-Louis Duval | 7 | 0 | 0.34 |
| Francisco Chinesta | 8 | 36 | 18.92 |