. The expectationconditionalmaximization either (ECME) algorithm has proven to be an effective way of accelerating the expectationmaximizationalgorithm for many problems. Recognizing the limitation of using prefixed...
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. The expectationconditionalmaximization either (ECME) algorithm has proven to be an effective way of accelerating the expectationmaximizationalgorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME implementation is what we call DECME-1, which uses the line that is determined by the two most recent estimates as the acceleration subspace. The investigation of DECME-1 leads to an efficient, simple, stable and widely applicable DECME implementation, which uses two-dimensional acceleration subspaces and is referred to as DECME-2. The fast convergence of DECME-2 is established by the theoretical result that, in a small neighbourhood of the maximum likelihood estimate, it is equivalent to a conjugate direction method. The remarkable accelerating effect of DECME-2 and its variant is also demonstrated with several numerical examples.
We introduced the spectrum-adapted expectation-conditionalmaximization (ECM) algorithm to improve the efficiency of the peak fitting of spectral data by various fitting models. The spectrum-adapted ECM algorithm can ...
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We introduced the spectrum-adapted expectation-conditionalmaximization (ECM) algorithm to improve the efficiency of the peak fitting of spectral data by various fitting models. The spectrum-adapted ECM algorithm can perform the peak fitting by using the Pseudo-Voigt mixture model and Doniach-& Scaron;unji & cacute;-Gauss mixture model which are generally used for the peak fitting in the X-ray photoelectron spectroscopy. Analyses of the synthetic and experimental spectral data showed that the proposed method quickly completed the calculation and estimated well-fitted curves to spectral data. This result suggests that the spectrum adapted ECM algorithm efficiently perform the peak fitting for large number of spectral data sets.
With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of res...
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With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning-based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation-conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank-Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P-f reduced to 0.0263% and the miss detection rate P-m reduced to 0.3466%.
P>Clustering is a widely used method in extracting useful information from gene expression data, where unknown correlation structures in genes are believed to persist even after normalization. Such correlation stru...
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P>Clustering is a widely used method in extracting useful information from gene expression data, where unknown correlation structures in genes are believed to persist even after normalization. Such correlation structures pose a great challenge on the conventional clustering methods, such as the Gaussian mixture (GM) model, k-means (KM), and partitioning around medoids (PAM), which are not robust against general dependence within data. Here we use the exponential power mixture model to increase the robustness of clustering against general dependence and nonnormality of the data. An expectation-conditional maximization algorithm is developed to calculate the maximum likelihood estimators (MLEs) of the unknown parameters in these mixtures. The Bayesian information criterion is then employed to determine the numbers of components of the mixture. The MLEs are shown to be consistent under sparse dependence. Our numerical results indicate that the proposed procedure outperforms GM, KM, and PAM when there are strong correlations or non-Gaussian components in the data.
Cluster analysis is a method that identifies similar groups of data without any prior knowledge of the relevant groups. One of the most widely used clustering methods is model-based clustering, in which data clusterin...
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Cluster analysis is a method that identifies similar groups of data without any prior knowledge of the relevant groups. One of the most widely used clustering methods is model-based clustering, in which data clustering is performed by fitting a probabilistic model to the data. Mixture of Gaussian distributions is a commonly used model in model-based clustering. Unfortunately, the number of covariance matrices parameters rapidly increases by increasing the number of variables or components in these models. So far, various classes of the parsimonious Gaussian mixture models, by applying various constraints on the covariance matrices, have been introduced to solve this problem. Unfortunately, the number of models in each of these classes is so small such that in practice it does not allow the study and selection of models with any number of parameters, which can vary between the minimum number (one parameter) and the maximum number (no constraints model) of parameters. In this paper, to deal with this problem a family of the parsimonious Gaussian mixture models is introduced. This is done by identifying and determining the appropriate partitions of the variances and correlation coefficients between variables among clusters. We call these models "the parsimonious Gaussian mixture models with partitioned parameters". The generalized expectation-conditional maximization algorithm, by employing the Fisher scoring method within the algorithm, is used to compute the maximum likelihood estimates of parameters. Bayesian information criterion is used for comparing and selecting the best model. Also, the steepest ascent method is adapted to search the best model. Finally, performances of these models are examined on two real datasets and a brief simulation study.
We propose a fitting model that automatically conducts the background subtraction during high-throughput peak fitting. The fitting model consists of the pseudo-Voigt mixture model and the ramp-sum background model, an...
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We propose a fitting model that automatically conducts the background subtraction during high-throughput peak fitting. The fitting model consists of the pseudo-Voigt mixture model and the ramp-sum background model, and each model represents the peak and background component, respectively. The optimization of the fitting model is performed by the spectrum adapted ECM algorithm that enables us to perform the peak fitting and background subtraction simultaneously through the high-throughput calculation. Application of the proposed model to the synthetic spectral data showed appropriate decomposition of the peak and background component. We also applied the proposed model to 3721 spectral data collected from the SnS sheet by X-ray photoelectron spectroscopy. The spectral data from the SnS sheet were successfully decomposed to the component of Sn 3d(3/2 )peak, Sn 3d(5/2 )peak and background, respectively. As the spectrum adapted ECM algorithm can efficiently analyze a large amount of spectral data, we can obtain the color map showing spatial distribution of Sn(II) and Sn(IV) using the parameter of Sn $$3{d_{5/2}}$$3d5/2 peak. The proposed model supports us to obtain the insightful spatial distribution of peak component that has been difficult to obtain by conventional peak fitting.
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