Handling noisy data in sparse model identification using subsampling and co-teaching

2021 ◽  
pp. 107628
Author(s):  
Fahim Abdullah ◽  
Zhe Wu ◽  
Panagiotis D. Christofides
Keyword(s):  
Model Identification ◽  
Noisy Data ◽  
Sparse Model

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Rubén Ibáñez ◽  
Emmanuelle Abisset-Chavanne ◽  
Amine Ammar ◽  
David González ◽  
Elías Cueto ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Model Identification ◽  
Curse Of Dimensionality ◽  
Data Driven ◽  
Multidimensional Data ◽  
High Dimensional ◽  
Proper Generalized Decomposition ◽  
High Dimensional Space ◽  
Sparse Model ◽  
Identification Technique

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.

Filtered Integral Formulation of the Sparse Model Identification Problem

2021 ◽  
pp. 1-16
Author(s):  
Damien Guého ◽  
Puneet Singla ◽  
Manoranjan Majji ◽  
Robert G. Melton
Keyword(s):  
Model Identification ◽  
Identification Problem ◽  
Integral Formulation ◽  
Sparse Model

Noisy Data: Incorporating Variability into Your Analysis

2014 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Richard Schwartz
Keyword(s):  
Noisy Data
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