6 papers
Reduced-Rank Estimation for Ill-Conditioned Stochastic Linear Model with High Signal-to-Noise Ratio
Tomasz Piotrowski, Isao Yamada
Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y=Hx+\sqrt{epsilon}n. In practical…
Performance of the stochastic MV-PURE estimator in highly noisy settings
Tomasz Piotrowski, Isao Yamada
The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-PURE estimator) has been developed to provide linear estimation with robustness against high n…
Localization of Brain Activity from EEG/MEG Using MV-PURE Framework
Tomasz Piotrowski, Jan Nikadon, Alexander Moiseev
We consider the problem of localization of sources of brain electrical activity from electroencephalographic (EEG) and magnetoencephalographic (MEG) measurements using spatial filt…
MV-PURE Spatial Filters with Application to EEG/MEG Source Reconstruction
Tomasz Piotrowski, Jan Nikadon, David Gutierrez
In this paper we propose spatial filters for a linear regression model which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sam…
Fixed points of nonnegative neural networks
Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor
We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors. We first show that nonnega…
Inverse Feasibility in Over-the-Air Federated Learning
Tomasz Piotrowski, Rafail Ismayilov, Matthias Frey +1
We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition…