A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

Rubén Ibáñez, Emmanuelle Abisset-Chavanne, Amine Ammar, David González, Elías Cueto, Antonio Huerta, Jean Louis Duval and Francisco Chinesta

Complexity, 2018, vol. 2018, 1-11

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5608286

DOI: 10.1155/2018/5608286

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