We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden ro...

Recently, mobile location estimation is drawing considerable attention in the field of wireless communications. Among different mobile location estimation methods, the one which estimates the location of mobile stations with reference to the wave propagation model is drawing much attention on the grounds that it is applicable to different kinds of cellular network. However, the signal propagati...

External memory (EM) algorithms are designed for computational problems in which the size of the internal memory of the computer is only a small fraction of the problem size. For certain large scale applications this is necessarily true. Typically, the cost models proposed for external memory algorithms have measured only the number of I/O operations, and the algorithms have been specially craf...

Image segmentation is one of the most important area of image retrieval. In colour image segmentation the feature vector of each image region is ’n’ dimension different from grey level image. In this paper a new image segmentation algorithm is developed and analyzed using the finite mixture of doubly truncated bivariate Gaussian distribution by integrating with the hierarchical clustering. The ...

It is well known that the likelihood sequence of the EM algorithm is nondecreasing and convergent (Dempster, Laird and Rubin (1977)), and that the limit points of the EM algorithm are stationary points of the likelihood (Wu (1982)), but the issue of the convergence of the EM sequence itself has not been completely settled. In this paper we close this gap and show that under general, simple, ver...

As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one often needs approximation inference if distribution assumption is beyond Gaussian distribution. In this paper, we propose a Bayesian linear regression model with Student-t assumptions (BLRS), which can be inferred exactl...

In this paper we present a generative probabilistic model of the appearance of a nonrigid object and an iterative procedure for searching for the maximum likelihood (ML) estimate of the position and shape of the tracked object in a new image. The shape of the object in an image is approximated by an ellipse that is described by a full covariance matrix. The appearance of the object is described...

Computerized tomography (CT) plays a critical role in modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging with less radiation exposure but without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling image reconstruction at lower radiation exposure levels with convergence analysis...

In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical sy...

We propose an algorithm for nonparametric estimation for finite mixtures of multivariate random vectors that is not, but that strongly resembles, a true EM algorithm. The vectors are assumed to have independent coordinates conditional upon knowing which mixture component from which they come, but otherwise their density functions are completely unspecified. Sometimes, the density functions may ...