Estimation of non-linear functionals of densities with confidence

This paper introduces a class of k-nearest neighbor (k-NN) estimators called bipartite plug-in (BPI) estimators for estimating integrals of non-linear functions of a probability density, such as Shannon entropy and R´enyi entropy. The density is assumed to be smooth, have bounded support, and be uniformly bounded from below on this set. Unlike previous k-NN estimators of non-linear density functionals, the proposed estimator uses data-splitting and boundary correction to achieve lower mean square error. Specifically, we assume that T i.i.d. samples X[subscript i] ∈ R[superscript d] from the density are split into two pieces of cardinality M and N respectively, with M samples used for computing a k-nearest-neighbor density estimate and the remaining N samples used for empirical estimation of the integral of the density functional. By studying the statistical properties of k-NN balls, explicit rates for the bias and variance of the BPI estimator are derived in terms of the sample size, the dimension of the samples and the underlying probability distribution. Based on these results, it is possible to specify optimal choice of tuning parameters M/T, k for maximizing the rate of decrease of the mean square error (MSE). The resultant optimized BPI estimator converges faster and achieves lower mean squared error than previous k-NN entropy estimators. In addition, a central limit theorem is established for the BPI estimator that allows us to specify tight asymptotic confidence intervals.Keywords: convergence rates, entropy estimation, concentration bounds, bias and variance tradeoff, adaptive estimators, data-splitting estimators, bipartite k-NN graphsThis is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE-Institute of Electrical and Electronics Engineers and can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=18

Image registration in high dimensional feature space

Image registration is a difficult task especially when spurrious image intensity differences and spatial variations between the two images are present. To robustify image registration algorithms to such spurrious variations it can be useful to employ an image registration matching criteria on higher dimensional feature spaces. This paper will present an overview of our recent work on image registration using high dimensional image features and entropic graph matching criteria. New entropic graph estimates of information divergence measures will be presented. We will demonstrate the advantage of our approach for ultrasound breast image registration