This paper investigates semi-blind channel estimation in massive multiple-input multiple-output (MIMO) systems using different priors on data symbols. We derive two tractable expectation-maximization (em) based channe...
详细信息
ISBN:
(纸本)9781538646588
This paper investigates semi-blind channel estimation in massive multiple-input multiple-output (MIMO) systems using different priors on data symbols. We derive two tractable expectation-maximization (em) based channel estimation algorithms;one based on a Gaussian prior and the other one based on a Gaussian mixture model (GMM) for the unknown data symbols. The numerical results show that the semiblind estimation schemes provide better channel estimates compared with the estimation based on training sequences only. The em algorithm with a Gaussian prior provides superior channel estimates compared to the em algorithm with a GMM prior in low signal-to-noise ratio (SNR) regime. However, the latter one outperforms the em algorithm with Gaussian prior as the SNR or as the number antennas at the base station (BS) increases. Furthermore, the performance of the semi-blind estimators become closer to the genie-aided maximum likelihood estimator based on known data symbols as the number of antennas at the BS increases.
Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typically leads to computationally challe...
详细信息
ISBN:
(纸本)9781728112954
Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typically leads to computationally challenging non-convex problems. While the Expectation-Maximization (em) algorithm is the most popular approach for solving these non-convex problems, the behavior of this algorithm is not well understood. In this work, we focus on the case of mixture of Laplacian (or Gaussian) distribution. We start by analyzing a simple equally weighted mixture of two single dimensional Laplacian distributions and show that every local optimum of the population maximum likelihood estimation problem is globally optimal. Then, we prove that the em algorithm converges to the ground truth parameters almost surely with random initialization. Our result extends the existing results for Gaussian distribution to Laplacian distribution. Then we numerically study the behavior of mixture models with more than two components. Motivated by our extensive numerical experiments, we propose a novel stochastic method for estimating the mean of components of a mixture model. Our numerical experiments show that our algorithm outperforms the Naive em algorithm in almost all scenarios.
A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewable sources and especially from solar power. One challenge with integrating solar power into the grid i...
详细信息
ISBN:
(纸本)9783319984438;9783319984421
A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewable sources and especially from solar power. One challenge with integrating solar power into the grid is that its power generation is stochastic and depends on various environmental factors. Thus, predicting future energy generation is important to moderate the overall energy requirements. In recent years, the use of machine learning approaches to solar power forecasting is becoming very popular. In this paper, a clustering based data segmentation approach is used to find natural subgrouping in the data. These subgroups are then used to construct forecasting models using various machine learning algorithms. The effectiveness of the approach is demonstrated by comparing the accuracy of clustering based forecasting to the standard forecasting models. The experimental results demonstrate that the proposed clustering based models produce more accurate models.
A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized li...
详细信息
A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the em algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and a real data example are used to illustrate the methods.
With density estimators it is possible to illustrate effectively the relation between concentration of different subpopulations living in certain areas and the care which is accordingly needed. In contrast to the conv...
详细信息
An approach is proposed for initializing the expectation-maximization (em) algorithm in multivariate Gaussian mixture models with an unknown number of components. As the em algorithm is often sensitive to the choice o...
详细信息
An approach is proposed for initializing the expectation-maximization (em) algorithm in multivariate Gaussian mixture models with an unknown number of components. As the em algorithm is often sensitive to the choice of the initial parameter vector, efficient initialization is an important preliminary process for the future convergence of the algorithm to the best local maximum of the likelihood function. We propose a strategy initializing mean vectors by choosing points with higher concentrations of neighbors and using a truncated normal distribution for the preliminary estimation of dispersion matrices. The suggested approach is illustrated on examples and compared with several other initialization methods. (C) 2011 Elsevier B.V. All rights reserved.
Considering numerous types of data, this paper discusses application of PCA to exponential family distributions. Reviewing the probabilistic basis of PCA, we propose a model using Laplace approximation, which was wide...
详细信息
ISBN:
(纸本)9783319959290;9783319959306
Considering numerous types of data, this paper discusses application of PCA to exponential family distributions. Reviewing the probabilistic basis of PCA, we propose a model using Laplace approximation, which was widely used in classification context, Laplace exponential family PCA (LePCA). The proposed approach provides a more probabilistic solution compared with numerous models before. Standard em algorithm can be applied to this model, while only a degraded form of em is applicable on previous exponential PCA models. LePCA absorbs probabilistic PCA, as well as the traditional PCA as its specialization by taking the Gaussian assumption for granted.
In advanced information technology of statistical analysis, often data for which there are no properties, parameters, characteristics and their values is found. In this situation, the actual becomes the problem of rec...
详细信息
In advanced information technology of statistical analysis, often data for which there are no properties, parameters, characteristics and their values is found. In this situation, the actual becomes the problem of recovering missing data. It's almost impossible to set a value which is missed, but there is a large number of simple and more complex methods for replacing these values. This study describes the characteristics of some methods of filling gaps and examples of their application to the tables of data and time series.
Mixed logit models with normally distributed random coefficients are typically estimated under the extreme assumptions that either the random coefficients are completely independent or fully correlated. A factor struc...
详细信息
Mixed logit models with normally distributed random coefficients are typically estimated under the extreme assumptions that either the random coefficients are completely independent or fully correlated. A factor structured covariance offers a range of alternatives between these two assumptions. However, because these models are more difficult to estimate they are not frequently used to model preference heterogeneity. This paper develops a simple expectation-maximization algorithm for estimating mixed logit models when preferences are generated from a factor structured covariance. The algorithm is easy to implement for both exploratory and confirmatory factor models. The estimator is applied to stated-preference survey data from residential energy customers (Train, 2007). Comparing the fit across five different models, which differed in their assumptions on the covariance of preferences, the results show that all three factor specifications produced a better fit of the data than the fully correlated model measured by BIC and two out of three performed better in terms of AIC.
Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships a...
详细信息
ISBN:
(纸本)9781538672471
Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.
暂无评论