Kullback-Leibler Approach to CUSUM Quickest Detection Rule for Markovian Time Series

A Kullback-leibler Distance Approach to System Identification

The use of probability in system identification is shown to be equivalent to measuring Kullback-Leibler distance between the actual (empirical) and model distributions of data. When data are not known completely (being compressed, quantized, aggregated, missing etc.), the minimum distance approach can be seen as an asymptotic approximation of probabilistic inference. A class of problems is poin...

Kullback-Leibler divergence approach to partitioned update Kalman filter

A Kullback-Leibler Approach to Gaussian Mixture Reduction

A common problem in multi-target tracking is to approximate a Gaussian mixture by one containing fewer components; similar problems can arise in integrated navigation. A common approach is successively to merge pairs of components, replacing the pair with a single Gaussian component whose moments up to second order match those of the merged pair. Salmond [1] and Williams [2], [3] have each prop...

Using Kullback-Leibler distance for performance evaluation of search designs

This paper considers the search problem, introduced by Srivastava cite{Sr}. This is a model discrimination problem. In the context of search linear models, discrimination ability of search designs has been studied by several researchers. Some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching only one possibl...

Kullback-Leibler Boosting

In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based on the sum of histogram divergences along corresponding global and discriminating linear features. Second, these linear features, called KL features, are iteratively learnt by maximizing the projected Kullback-Leibler d...