Topic
Expectation–maximization algorithm
About: Expectation–maximization algorithm is a research topic. Over the lifetime, 11823 publications have been published within this topic receiving 528693 citations. The topic is also known as: EM algorithm & Expectation Maximization.
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13 Oct 2003
TL;DR: A framework for texture recognition based on local affine-invariant descriptors and their spatial layout is presented and initial probabilities computed from the generative model are refined using a relaxation step that incorporates co-occurrence statistics.
Abstract: We present a framework for texture recognition based on local affine-invariant descriptors and their spatial layout. At modelling time, a generative model of local descriptors is learned from sample images using the EM algorithm. The EM framework allows the incorporation of unsegmented multitexture images into the training set. The second modelling step consists of gathering co-occurrence statistics of neighboring descriptors. At recognition time, initial probabilities computed from the generative model are refined using a relaxation step that incorporates co-occurrence statistics. Performance is evaluated on images of an indoor scene and pictures of wild animals.
142 citations
TL;DR: The fitting of finite mixture models via the EM algorithm is considered for data which are available only in grouped form and which may also be truncated.
Abstract: The fitting of finite mixture models via the EM algorithm is considered for data which are available only in grouped form and which may also be truncated. A practical example is presented where a mixture of two doubly truncated log-normal distributions is adopted to model the distribution of the volume of red blood cells in cows during recovery from anemia.
142 citations
142 citations
TL;DR: An algorithm is suggested and it is shown that this algorithm converges to the solution of the minimization problem and a simulation study is presented, showing the superiority of the algorithm compared to the EM algorithm in the interval censoring case 2 setting.
Abstract: The problem of minimizing a smooth convex function over a specific cone in IRn is frequently encountered in nonparametric statistics. For that type of problem we suggest an algorithm and show that this algorithm converges to the solution of the minimization problem. Moreover, a simulation study is presented, showing the superiority of our algorithm compared to the EM algorithm in the interval censoring case 2 setting.
142 citations
TL;DR: In this paper, a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities is used to analyze the relationship between patents and research and development spending at the firm level.
Abstract: Count-data models are used to analyze the relationship between patents and research and development spending at the firm level, accounting for overdispersion using a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities. Maximum likelihood estimation using the EM and quasi-Newton algorithms is discussed. Monte Carlo studies suggest that (a) penalized likelihood criteria are a reliable basis for model selection and can be used to determine whether continuous or finite support for the mixing distribution is more appropriate and (b) when the mixing distribution is incorrectly specified, parameter estimates remain unbiased but have inflated variances.
142 citations