In this paper we address the problem of clustering sets of curve or trajectory data generated by groups of objects or individuals. The focus is to model curve data directly using a set of model-based curve clustering algorithms referred to as mixtures of regressions or regression mixtures. The proposed methodology is based on extension to regression mixtures that we call random effects regressi...

On the basis of order statistics, Baker (2008) proposed a method for constructing multivariate distributions with fixed marginals. This is another representation of the Bernstein copula. According to the construction of Baker’s distribution, the Bernstein copula can be regarded as a finite mixture distribution. In this paper, we propose expectationmaximization (EM) algorithms to estimate the Be...

We present an efficient object detection and tracking technique using still cameras in low contrast conditions. The tracking algorithm involves background subtraction using Gaussian Mixture Model (GMM). Our method involves updating the parameters of the Mixture Model using a combination of an online k-means approximation technique and the ExpectationMaximization (EM) algorithm. We have shown ex...

We build up the mathematical connection between the ”ExpectationMaximization” (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special ...

We derive a multi-user receiver that performs joint data detection and channel estimation (JDE) of DS-CDMA signals. The proposed sub-optimal receiver is formulated within the framework of the space-alternating generalized expectationmaximization (SAGE) algorithm. The time-varying channel is represented by discrete prolate spherical (DPS) sequences. The resulting receiver iterates between MMSE b...

We consider the issue of modeling the latent or hidden exposure occurring through either incomplete data or an unobserved underlying risk factor. We use the celebrated expectationmaximization (EM) algorithm as a convenient tool in detecting latent (unobserved) risks in finite mixture models of claim severity and in problems where data imputation is needed. We provide examples of applicability o...

Several authors in recent years have proposed discrete analogues to principle component analysis intended to handle discrete or positive only data, for instance suited to analyzing sets of documents. Methods include non-negative matrix factorization, probabilistic latent semantic analysis, and latent Dirichlet allocation. This paperbegins with a review of the basic theory of the variational ext...

In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationmaximization (EM) algorithm. In this paper, we extend the FMLLR structure from diagonal to unrestricted full matrix with a sophisticated algorithm for the training of re...

This paper presents the problem of estimating the parameters of a given number of superimposed signals, as is the case of the received signal in wireless communications. Based on the description of the received signal in the frequency domain, one version of the SAGE (Space-Alternating Generalized ExpectationMaximization) algorithm is presented, allowing the estimation, for each impinging ray, t...

Driver support systems of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing...