Statistics and optimization in high dimensions for spatio-temporal data


Séminaire Données et Aléatoire Théorie & Applications

16/04/2020 - 14:00 Mr Vadim Strijov (Moscow Institute of Physics and Technology) Salle 106 - Batiment IMAG

This seminar discusses dimensionality reduction problem for signal decoding. The challenge is redundancy in the data description. High correlations among measurements of complex signals lead to multiple correlations. This case studies correlations in both input and target spaces that carry heterogenous data. The seminar enlightens feature selection algorithms to construct simple and stable forecasting model. It extends ideas of the quadratic programming feature selection approach and selects non-correlated features that are relevant to the target. The proposed methods take into account  dependencies in both design and target space and select features, which fit both spaces jointly. The computational experiment was carried out using an electrocorticogram dataset. The obtained model predicts hand motions using signals of the brain cortex.