Performance bounds for coupled models

Chengfang Ren, Rodrigo Cabral Farias, Pierre-Olivier Amblard, Pierre Comon
2016 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Two models are called "coupled" when a non empty set of the underlying parameters are related through a differentiable implicit function. The goal is to estimate the parameters of both models by merging all datasets, that is, by processing them jointly. In this context, we show that the parameter estimation accuracy under a general class of dataset distributions always improves when compared to an equivalent uncoupled model. We eventually illustrate our results with the fusion of multiple
more » ... n of multiple tensor data.
doi:10.1109/sam.2016.7569651 dblp:conf/ieeesam/RenFAC16 fatcat:mljw3islvjhypczsd4vwpbs7om