Abstract

ABSTRACT We consider the problem of assigning a class label to the noisy output of a linear system, where clean feature examples are available for training. We design a robust classifier that operates on a linear estimate, with uncertainty modeled by a Gaussian distribution with parameters derived from the bias and covariance of a linear estimator. Class-conditional distributions are modeled locally as Gaussians. Since estimation of Gaussian parameters from few training samples can be illposed, we extend recent work in Bayesian quadratic discriminant analysis to derive a robust local generative classifier. Experiments show a statistically significant improvement over prior art.