Publication

Diffusion-based bias-compensated RLS for distributed estimation over adaptive sensor networks

Publications associées (85)

Joint estimation of RETF vector and power spectral densities for speech enhancement based on alternating least squares

The multi-channel Wiener filter (MWF) is a well-known multi-microphone speech enhancement technique, aiming at improving the quality of the recorded speech signals in noisy and reverberant environments. Assuming that reverberation and ambient noise can be ...

2019

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Nicolas Henri Bernard Flammarion, Aymeric Daphnis Kévin Dieuleveut

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2017

Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-based Real-Time State Estimation

Mario Paolone, Paolo Romano, Marco Pignati, Lorenzo Zanni

We intend to prove that PMU-based state estimation processes for active distribution networks exhibit unique time determinism and refresh rate that make them suitable to satisfy the time-critical requirements of protections as well as the accuracy requirem ...

Institute of Electrical and Electronics Engineers2017

2015

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade-offs, but t ...

Wiley-Blackwell2015

We study the accuracy of the discrete least-squares approximation on a finite-dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

2015

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

MATHICSE2015

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

2015

We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the para ...

2014

Diffusion adaptation techniques based on the least-mean-squares criterion have been proposed for distributed detection of a signal in Gaussian-distributed noise, forgoing the need for a fusion center. However, least-mean-squares solutions are generally non ...

IEEE2014

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