On robust parameter estimation in brain–computer interfacing

On robust parameter estimation in brain-computer interfacing.

OBJECTIVE The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks...

Robust Parameter Estimation in ComputerVisionCharles

Robust Drift Parameter Estimation In Diffusion Processes

We consider some inference problems concerning the drift parameters vector of diffusion process. Namely, we consider the case where the parameters vector is suspected to satisfy certain restriction. Under such a design and imprecise prior information, we propose Stein-rule (or shrinkage) estimators which improves over the performance of the classical maximum likelihood estimator (MLE). By using...

Robust Parameter Estimation in Computer Vision

Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, ...

Robust parameter estimation for mixture model

In pattern recognition, when the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors with as many as several hundred spectral bands. While the new sensor technology prov...