In this article, a method based on a non-parametric estimation of the Kullback–Leibler divergence using a local feature space is proposed for synthetic aperture radar (SAR) image change detection. First, local features based on a set of Gabor filters are extracted from both preand post-event images. The distribution of these local features from a local neighbourhood is considered as a statistic...

We provide background information to allow a heuristic understanding of two types of criteria used in selecting a model for making inferences from ringing data. The first type of criteria (e.g., AIC, AIC, QAIC and TIC) are estimates of (relative) Kullback-Leibler information or-distance and attempt to select a good approximating model for inference, based on the Principle of Parsimony. The seco...

Concatenative speech synthesis systems attempt to minimize audible signal discontinuities between two successive concatenated units. An objective distance measure which is able to predict audible discontinuities is therefore very important, particularly in unit selection synthesis, for which units are selected from among a large inventory at run time. In this paper, we describe a perceptual tes...

Probabilistic distance measures are important quantities in many research areas. For example, the Chernoff distance (or the Bhattacharyya distance as its special example) is often used to bound the Bayes error in a pattern classification task and the Kullback-Leibler (KL) distance is a key quantity in the information theory literature. However, computing these distances is a difficult task and ...

For the application of many statistical methods it is necessary to establish the measure of di erence to be used This measure has to be de ned in accordance with the nature of the data In this study we propose a measure of di erence when the data set is compositional We analyze its properties and we present examples to illustrate its performance INTRODUCTION It is well known that the usual diss...

Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent properties are needed to choose the right dissimilarity measure for the problem a...

Deconvolution is usually regarded as one of the so called ill-posed problems of applied mathematics if no constraints on the unknowns can be assumed. In this paper, we discuss the idea of well-de ned statistical models being a counterpart of the notion of well-posedness. We show that constraints on the unknowns such as non-negativity and sparsity can help a great deal to get over the inherent i...

In this paper, we propose a Generalized Approximate Cross Validation (GACV) function for estimating the smoothing parameter in the penalized log likelihood regression problem with non-Gaussian data. This GACV is obtained by, first, obtaining an approximation to the leaving-out-one function based on the negative log likelihood, and then, in a step reminiscent of that used to get from leaving-out...