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Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2013年第504期108卷 1366-1384页

作者： Zhang, Jian Univ Kent Sch Math Dept Stat & Actuarial Sci Canterbury CT2 7NF Kent England

Most clustering methods assume that the data can be represented by mutually exclusive clusters, although this assumption may not be the case in practice. For example, in gene expression microarray studies, investigators have often found that a gene can play multiple functions in a cell and may, therefore, belong to more than one cluster simultaneously, and that gene clusters can be linked to each other in certain pathways. This article examines the effect of the above assumption on the likelihood of finding latent clusters using theoretical calculations and simulation studies, for which the epistatic structures were known in advance, and on real data analyses. To explore potential links between clusters, we introduce an epistatic mixture model which extends the Gaussian mixture by including epistatic terms. A generalized expectation-maximization (em) algorithm is developed to compute the related maximum likelihood estimators. The Bayesian information criterion is then used to determine the order of the proposed model. A bootstrap test is proposed for testing whether the epistatic mixture model is a significantly better fit to the data than a standard mixture model in which each data point belongs to one cluster. The asymptotic properties of the proposed estimators are also investigated when the number of analysis units is large. The results demonstrate that the epistatic links between clusters do have a serious effect on the accuracy of clustering and that our epistatic approach can substantially reduce such an effect and improve the fit.

关键词： Asymptotic property Bootstrap test Epistatic link Finite epistatic mixture generalized em algorithm Model-based epistatic clustering

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Chimeral Clustering

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JOURNAL OF CLASSIFICATION 2022年第1期39卷 171-190页

作者： Hou-Liu, Jason Browne, Ryan P. Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON Canada

Hybrid species tend to exhibit a mixture of parent characteristics;we propose chimeral clusters as exhibiting a mixture of parent parameters, a type of intercluster structure. Morphometric measurements in the iris dataset describe the hybrid Iris versicolor as intermediate to those of parent species Iris setosa and Iris virginica, which motivates our extension of Gaussian mixture models to allow mixing in the parameter space. We propose a mixing mechanism whereby chimeral clusters are parameterized by a convex combination of fully varying prototype cluster parameters and characterize the identifiability of the postulated mixture model. Estimation of chimeral clustering models is described using variations of the expectation-maximization algorithm and the solution to the continuous-time algebraic Riccati equation. The efficacy of chimeral clustering is demonstrated using morphometric datasets describing iris, Cooper's hawks, and water striders, with comparisons to typical Gaussian mixture models. We evaluate parameter recovery on a synthetic dataset and demonstrate that parsimonious covariance matrices and chimeral clustering capture different kinds of intercluster structure.

关键词： Intercluster structure Model-based clustering generalized em algorithm Parsimony Dimensionality reduction

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20th IEEE International Conference on Image Processing (ICIP)

作者： Pan, Lili Liu, Risheng Xie, Mie Univ Elect Sci & Technol China Sch Elect Engn Beijing Peoples R China Dalian Univ Technol Dept Biomed Engn Dalian Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479923410

Mixture of regressions is one of the most well-known statistical techniques for the problem of head pose estimation. However, conventional approaches are often sensitive to noise and suffer from underdetermined problem when the training data is insufficient (i.e., the number of training samples for some regressors is less than the dimensionality of the image features). In this paper, we propose a novel approach, named mixture of related regressions (MReR) to address above limitations. By imposing an additional similarity constraint on related regressors, MReR can significantly enhance robustness and avoid uncertainty for head pose estimation. As a nontrivial byproduct, we also develop an em-type algorithm to efficiently solve the MReR model. Experimental results on both synthetic and real-world datasets demonstrate the benefits of MReR.

关键词： Mixture of regressions relatedness analysis generalized em algorithm head pose estimation

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Cross-reference maximum likelihood estimate reconstruction for positron emission tomography

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Biomedical Engineering - Applications, Basis and Communications 2001年第4期13卷 190-198页

作者： Chen, C.-M. Lu, H.H.-S. Hsu, Y.-P. Institute of Biomedical Engineering National Taiwan University Taipei Taiwan

Maximum likelihood estimate (MLE) is a widely used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate, we propose a new and efficient method in this paper to incorporate the correlated but possibly incomplete structure information which may be derived from expertise, PET systems or other imaging modalities. A mean estimate smoothing the MLE locally within each region of interest is derived according to the boundaries provided by the structure information. Since the boundaries may not be correct, a penalized MLE using the mean estimate is sought. The resulting reconstruction is called a cross-reference maximum likelihood estimate (CRMLE). The CRMLE can be obtained through a modified em algorithm, which is computation and storage efficient. By borrowing the strength from the correct portion of boundary information, the CRMLE is able to extract the useful information to improve reconstruction for different kinds of incomplete and incorrect boundaries in Monte Carlo studies. The proposed CRMLE algorithm not only reduces the estimation errors, but also preserves the correct boundaries. The penalty parameters can be selected through human interactions or automatically data-driven methods, such as the generalized cross validation method.

关键词： Cross-validation generalized generalized em algorithm Maximum likelihood estimate Regularization

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Active Visual Recognition with Expertise Estimation in Crowdsourcing

Active Visual Recognition with Expertise Estimation in Crowd...

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IEEE International Conference on Computer Vision (ICCV)

作者： Long, Chengjiang Hua, Gang Kapoor, Ashish Stevens Inst Technol Hoboken NJ 07030 USA Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781479928392

We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noises and the expertise level of each individual labeler in two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized em algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy and estimated expertise for active selection of data sample to be labeled, and active selection of high quality labelers to label the data, respectively. We apply the proposed model for three visual recognition tasks, i.e, object category recognition, gender recognition, and multi-modal activity recognition, on three datasets with real crowd-sourced labels from Amazon Mechanical Turk. The experiments clearly demonstrated the efficacy of the proposed model.

关键词： Active Learning Crowdsourcing Expectation Propagation Gaussian Processes Visual Recognition Gaussian processes Experiential Learning Crowdsource Noise active vision Probabilistic Model Recognition (Psychology) generalized em algorithm Flip

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an extension to the parsimonious topic modeling

an extension to the parsimonious topic modeling

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作者： Chen, Yezhou PennState University Libraries

学位级别：Master of Science

In this thesis we develop a new model for estimating topics based on parsimonious topic modeland Latent Dirichlet Allocation. In parsimonious models, each word has a topic shared occurringprobability or a topic specific occurring probability for each topic and this is controlled by a *** our model, we use one more switch set to identify the mentioned switch subset(all switches forone word in all topics) by one of three cases: the word has a topic shared occurring probabilityfor all topics, the word has a topic specific occurring probability for all topics, the word has atopic shared occurring probability for some topics and a topic specific occurring probability forsome topics. We use a generalized Expectation-Maximization algorithm as a learning algorithmto optimize the parameters and minimize the objective function. Numerical results are presentedto examine the performance of such a model.

关键词： Bayesian Information Criterion(BIC) Model selection Parsimonious topic models generalized em algorithm

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Identification of Linear Parameter Varying Systems with Missing Output Data Using generalized Expectation-Maximization algorithm

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IFAC Proceedings Volumes 2014年第3期47卷 9364-9369页

作者： X. Yang W. Xiong B. Huang H. Gao Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi Jiangsu 214122 China Department of Chemical and Materials Engineering University of Alberta Edmonton Alberta T6G 2G6 Canada Research Institute of Mechatronics and Automation Bohai University Jinzhou 121013 China and Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China

This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinear industrial process, the multiple-model LPV model in which the global model is constructed by smoothly weighted combination of multiple local models is considered here. The problem of missing output variables data is commonly encountered in practice. In order to handle the multiple-model identification problems of LPV systems with incomplete data, the local model is taken to have a finite impulse response (FIR) model structure and the generalized expectation-maximization (em) algorithm is adopted to estimate the unknown parameters of the global LPV model. To avoid the problems of ill-conditioned matrices and high sensitivity of parameters to noise, the prior information on the coefficients of each local FIR model is employed to construct the prior probability of unknown parameters. Then the maximum a posteriori (MAP) estimates of the global model parameters are derived via the generalized em algorithm. The numerical example is presented to demonstrate the effectiveness of the proposed method.

关键词： Linear parameter varying systems generalized em algorithm Missing output data Maximum a posteriori estimates

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A Quasi-empirical Bayes Method for Small Area Estimation

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Quarterly Publications of the American Statistical Association 1993年第424期88卷 1444-1448页

This article develops an empirical Bayes-type approach for small area estimation based only on the specification of a set of conditionally independent hierarchical mean and variance functions describing the first two moments of the process generating the data. The objective of the analysis is to draw inference about the intermediate or the random means. We combine the quasi-likelihood functions to construct the quasi-posterior density of the random means conditional on the data and the marginal parameters. We describe a method for estimating the marginal parameters that are then substituted in the quasi-posterior density of the random means. The empirical quasi-posterior density so derived is used to obtain the quasi-empirical Bayes estimates of the random parameters. We apply the methodology to estimate the utilization rates of cancer chemotherapy for the selected counties in the state of Washington.

关键词： Estimating equations generalized em algorithm Jackknife Marginal likelihood Method of moments

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Asynchronous Hybrid Maximum Likelihood Classification of Linear Modulations

Asynchronous Hybrid Maximum Likelihood Classification of Lin...

引用

IEEE Global Communications Conference

作者： Onur Ozdemir Pramod K. Varshney Wei Su Boston Fusion Corp. Department of EECS Syracuse University Army CERDEC Aberdeen Proving Ground

ISBN: (纸本)9781479913510

In this paper, we consider the problem of linear modulation classification in the presence of unknown time off-set, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a generalized Expectation Maximization (Gem) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The Gem algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the Gem algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.

关键词： Modulation classification Time offset Hybrid maximum likelihood generalized expectation maximization generalized em algorithm linear modulation maximum likelihood signal to noise ratios Taxonomy Quadrature amplitude modulation

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IEEE International Conference on Image Processing

作者： Lili Pan Risheng Liu Mei Xie School of Electronic Engineering University of Electronic Science and Technology of China P. R. China Department of Biomedical Engineering Dalian University of Technology P. R. China

ISBN: (纸本)9781479923427

关键词： Mixture of regressions relatedness analysis generalized em algorithm head pose estimation generalized em algorithm Head Disease Predictive Factor mixtures Posing pose estimation

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