Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （em） algorithm, VA-accelerated em algorithm, whose convergence speed is faster than that of em algorithm. The VA-accelerated ...
详细信息
Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （em） algorithm, VA-accelerated em algorithm, whose convergence speed is faster than that of em algorithm. The VA-accelerated em algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the em algorithm, thus it keeps the flexibility and simplicity of the em algorithm. Considering Steffensen iterative process, we have also given the Steffensen form of the VA-accelerated em algorithm. It can be proved that the reform process is quadratic convergence. Numerical analysis illustrate the proposed methods are efficient and faster than em algorithm.
Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In...
详细信息
Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71dB for Moffett database and 16.26dB for HYDICE database.
An em algorithm was used to analyse data arising from non-linear mixed-effects models. The fixed parameters were determined by maximum likelihood using simplex minimization, and the random effects were estimated using...
详细信息
An em algorithm was used to analyse data arising from non-linear mixed-effects models. The fixed parameters were determined by maximum likelihood using simplex minimization, and the random effects were estimated using the em algorithm after linearization with respect to the random effects. Applications to a simple linear model and population pharmacokinetics are described. The use of posterior parameter estimates to investigate covariate relationships is briefly described. The implementation of the estimation-maximization (em) algorithm described here has proved in practice to be robust but slow. We intend to use a Newton-Raphson minimization routine in place of the simplex method to hasten convergence. The alternative linearization of the non-linear mixed effects model suggested by Lindstrom and Bates (Biometrics 46 (1990) 673-687) is much more unstable than the usual linearization, especially during the initial iterations. In the case of indomethacin the two linearizations produced very similar results. The individual posterior parameter estimates provided by the program are very useful for the detection of covariate relationships in population pharmacokinetic studies. In addition, the posterior means can be used in the estimation of pharmacokinetic-pharmacodynamic relationships from sparse pharmacokinetic data where individual modelling is impossible.
Background We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metas...
详细信息
Background We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness. In this article, using simulations, we compare two estimation approaches that we considered for our model: use of a constrained quasi-Newton algorithm to maximize the likelihood conditional on the random effect, and an Expectation-Maximization algorithm where we further condition the random-effect distribution on the data. Results In some specific settings, especially with sparse information, the estimation of the parameter of interest is at the boundary a non-negligible number of times using the first approach, while the em algorithm gives more satisfactory estimates. This is of considerable importance for our application, since an estimate of either 0 or 1 for the proportion of cases that are clonal leads to individual probabilities being 0 or 1 in settings where the evidence is clearly not sufficient for such definitive probability estimates. Conclusions The em algorithm is a preferable approach for our clonality random-effect model. It is now the method implemented in our R package Clonality, making available an easy and fast way to estimate this model on a range of applications.
This contribution is devoted to the estimation of the parameters of multivariate Gaussian mixture where the covariance matrices are constrained to have a linear structure such as Toeplitz, Hankel, or circular constrai...
详细信息
This contribution is devoted to the estimation of the parameters of multivariate Gaussian mixture where the covariance matrices are constrained to have a linear structure such as Toeplitz, Hankel, or circular constraints. We propose a simple modification of the expectation-maximization (em) algorithm to take into account the structure constraints. The basic modification consists of virtually updating the observed covariance matrices in a first stage. Then, in a second stage, the estimated covariances undergo the reversed updating. The proposed algorithm is called the inverse em algorithm. The increasing property of the likelihood through the algorithm iterations is proved. The strict increasing for nonstationary points is proved as well. Numerical results are shown to corroborate the effectiveness of the proposed algorithm for the joint unsupervised classification and spectral estimation of stationary autoregressive time series.
The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device, There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing t...
详细信息
The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device, There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing to interpret bar code signals, These methods, nevertheless, fundamentally limit label reading and cannot be extended, A new method based on the deterministic em algorithm Is described here, First results show a substantial improvement in label reading depth of field, which is an important performance parameter for bar code readers.
Finite mixture models represent one of the most popular tools for modeling heterogeneous data. The traditional approach for parameter estimation is based on maximizing the likelihood function. Direct optimization is o...
详细信息
Finite mixture models represent one of the most popular tools for modeling heterogeneous data. The traditional approach for parameter estimation is based on maximizing the likelihood function. Direct optimization is often troublesome due to the complex likelihood structure. The expectation-maximization algorithm proves to be an effective remedy that alleviates this issue. The solution obtained by this procedure is entirely driven by the choice of starting parameter values. This highlights the importance of an effective initialization strategy. Despite efforts undertaken in this area, there is no uniform winner found and practitioners tend to ignore the issue, often finding misleading or erroneous results. In this paper, we propose a simple yet effective tool for initializing the expectation-maximization algorithm in the mixture modeling setting. The idea is based on model averaging and proves to be efficient in detecting correct solutions even in those cases when competitors perform poorly. The utility of the proposed methodology is shown through comprehensive simulation study and applied to a well-known classification dataset with good results.
Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generally not obvious, and when it occurs, infer...
详细信息
Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generally not obvious, and when it occurs, inference methods are rather specific to the mixture model under consideration. Hence, a generalization of the em algorithm to semiparametric mixture models is proposed. The approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the proposed em type estimators is studied numerically not only through several Monte-Carlo experiments but also through comparison with alternative methods existing in the literature. In addition to these numerical experiments, applications to real data are provided, showing that the estimation method behaves well, that it is fast and easy to be implemented. (C) 2006 Elsevier B.V. All rights reserved.
With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition acc...
详细信息
With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition accuracy. Therefore, an improved expectation maximization algorithm based on the constraint matrix Z and Tsallis entropy is proposed. The core goal of this algorithm is to accelerate the convergence of classification and improve accuracy. Furthermore, to enhance the classification accuracy, the spatial expectation maximization algorithm is introduced, which innovatively converts the sample mean and covariance matrix into L-1 -median and modified rank covariance matrix. According to the experimental data, the recall rate of the original expectation maximization algorithm is only 74%. However, the recall rate of the spatial expectation maximization algorithm in the Attack service has significantly increased to 85%. In other tests, such as Www and Peer-to-peer services, the recall rate has also significantly improved, increasing from 96% and 95.3% to 97.7% and 96.1%, respectively. These experimental results highlight the superior robustness of the spatial expectation maximization algorithm in handling outlier data. It further proves the outstanding performance in improving the accuracy of network traffic recognition. This research has brought significant innovation and potential practical value to the network traffic identification.
We study a modification of the emS algorithm in which each step of the emS algorithm is preceded by a nonlinear smoothing step of the form Nf = exp(S* log f), where S is the smoothing operator of the emS algorithm. In...
详细信息
We study a modification of the emS algorithm in which each step of the emS algorithm is preceded by a nonlinear smoothing step of the form Nf = exp(S* log f), where S is the smoothing operator of the emS algorithm. In. the context of positive integral equations (a la positron emission tomography) the resulting algorithm is related to a convex minimization problem which always admits a unique smooth solution, in contrast to the unmodified maximum likelihood setup. The new algorithm has slightly stronger monotonicity properties than the original em algorithm. This suggests that the modified emS algorithm is actually an em algorithm for the modified problem. The existence of a smooth solution to the modified maximum likelihood problem and the monotonicity together imply the strong convergence of the new algorithm. We also present some simulation results for the integral equation of stereology, which suggests that the new algorithm behaves roughly like the emS algorithm.
暂无评论