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Fast Estimation of Ideal Points with Massive Data

AMERICAN POLITICAL SCIENCE REVIEW 2016年第4期110卷 631-656页

作者： Imai, Kosuke Lo, James Olmsted, Jonathan Princeton Univ Dept Polit Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA Univ Southern Calif Dept Polit Sci Los Angeles CA 90089 USA NPD Grp Port Washington NY 11050 USA

Estimation of ideological positions among voters, legislators, and other actors is central to many subfields of political science. Recent applications include large data sets of various types including roll calls, surveys, and textual and social media data. To overcome the resulting computational challenges, we propose fast estimation methods for ideal points with massive data. We derive the expectation-maximization (EM) algorithms to estimate the standard ideal point model with binary, ordinal, and continuous outcome variables. We then extend this methodology to dynamic and hierarchical ideal point models by developing variational EM algorithms for approximate inference. We demonstrate the computational efficiency and scalability of our methodology through a variety of real and simulated data. In cases where a standard Markov chain Monte Carlo algorithm would require several days to compute ideal points, the proposed algorithm can produce essentially identical estimates within minutes. Open-source software is available for implementing the proposed methods.

关键词： expectation-maximization algorithms IDEOLOGY VOTERS -- Attitudes BIG data DATA analysis

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Automatic design of an effective image filter based on an evolutionary algorithm for venous analysis

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2016年第1期5卷 1-1页

作者： Kashihara, Koji Iwase, Masashi Univ Tokushima Inst Technol & Sci 2-1 Minamijyousanjima Tokushima 7708506 Japan Univ Tokushima Dept Informat Sci & Intelligent Syst 2-1 Minamijyousanjima Tokushima 7708506 Japan

Medical doctors and clinical technologists operate specific, complicated diagnostic systems to assess venous diseases. Instead of using such expensive equipment, low-cost infrared cameras can capture vein images noninvasively and simply. However, the recorded image has a possibility to result in low contrast and a low signalto-noise (S/N) ratio. An effective image filtering method to estimate venous changes will solve this problem and enable the early detection of disease. For this study, a novel filtering method based on the genetic algorithm (GA) with the expectation-maximization algorithm was proposed for the visualization of vein shapes;its effectiveness was evaluated by images acquired from a near-infrared (780 nm) camera. The novel filter was able to be automatically designed by the GA to improve the worse S/N ratio of vein images, with an unknown correct answer image. If the proposed filtering method is incorporated into e-healthcare applications, it could be widely distributed through smartphones or tablets and facilitate finding abnormal veins at an early stage.

关键词： expectation-maximization algorithms Gaussian mixture model Genetic algorithms Filters Infrared image sensors

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On Possibilistic Clustering algorithms Based on Noise Clustering

On Possibilistic Clustering Algorithms Based on Noise Cluste...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Yuchi Kanzawa Department of Communications Engineering Shibaura Institute of Technology Tokyo JAPAN

ISBN: (纸本)9781509026791

In this study, several possibilistic clustering methods are proposed based on noise clustering. The proposed methods are motivated by the fact that conventional possibilistic clustering methods do not correspond with noise clustering methods in entropy-regularized situations, whereas these methods do correspond in Bezdek-type fuzzified situation.

关键词： Clustering methods Optimization Entropy Clustering algorithms Mixture models Intelligent systems expectation-maximization algorithms

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A novel essential domain perspective for exploring gene essentiality

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BIOINFORMATICS 2015年第18期31卷 2921-2929页

作者： Lu, Yao Lu, Yulan Deng, Jingyuan Peng, Hai Lu, Hui Lu, Long Jason Shanghai Jiao Tong Univ Shanghai Childrens Hosp Shanghai Inst Med Genet Shanghai 200040 Peoples R China Fudan Univ Sch Life Sci Inst Biostat State Key Lab Genet Engn Shanghai 200433 Peoples R China Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Jianghan Univ Inst Syst Biol Wuhan Hubei Peoples R China Univ Illinois Dept Bioengn MC 063 Chicago IL 60607 USA Sichuan Univ West China Hosp Collaborat Innovat Ctr Biotherapy Chengdu 610064 Peoples R China

Motivation: Genes with indispensable functions are identified as essential;however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain. Results: To identify such essential domains, we have developed an expectation-maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10-23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred fromribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains.

关键词： GENETIC research PROTEINS -- Analysis POLYPEPTIDES expectation-maximization algorithms PREDICTION models

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Hyperspectral Unmixing Via Turbo Bilinear Approximate Message Passing

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2015年第3期1卷 143-158页

作者： Vila, Jeremy Schniter, Philip Meola, Joseph Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA US Air Force Res Lab Multispectral Sensing Div Dayton OH 45433 USA

The goal of hyperspectral unmixing is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels into N constituent material spectra (or "endmembers") with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing based on loopy belief propagation (BP) that enables the exploitation of spectral coherence in the endmembers and spatial coherence in the abundances. In particular, we partition the factor graph into spectral coherence, spatial coherence, and bilinear subgraphs, and pass messages between them using a "turbo" approach. To perform message passing within the bilinear subgraph, we employ the bilinear generalized approximate message passing algorithm (BiG-AMP), a recently proposed belief-propagationbased approach to matrix factorization. Furthermore, we propose an expectation-maximization (EM) strategy to tune the prior parameters and a model-order selection strategy to select the number of materials N. Numerical experiments conducted with both synthetic and real-world data show favorable unmixing performance relative to existing methods.

关键词： Approximate message passing belief propagation expectation-maximization algorithms hyperspectral imaging

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Effect of Residual Channel Estimation Errors in Random Access Methods for Satellite Communications 81

Effect of Residual Channel Estimation Errors in Random Acces...

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81st IEEE Vehicular Technology Conference (VTC Spring)

作者： Zidane, Karine Lacan, Jerome Boucheret, Marie-Laure Poulliat, Charly Gineste, Mathieu Roques, Damien Bes, Caroline Deramecourt, Arnaud Univ Toulouse ISAE DMIA Toulouse France Univ Toulouse TeSA Toulouse France Univ Toulouse IRIT ENSEEIHT Toulouse France Thales Alenia Space Toulouse France CNES Toulouse France

ISBN: (纸本)9781479980888

In recent random access methods used for satellite communications, collisions between packets are not considered as destructive. In fact, to deal with the collision problem, successive interference cancellation is performed at the receiver. Generally, it is assumed that the receiver has perfect knowledge of the interference. In practice, the interference term is affected by the transmission channel parameters, i.e., channel attenuation, timing offsets, frequency offsets and phase shifts, and needs to be accurately estimated and canceled to avoid performance degradation. In this paper, we study the performance of an enhanced channel estimation technique combining estimation using an autocorrelation based method and the expectation-maximization algorithm integrated in a joint estimation and decoding scheme. We evaluate the effect of residual estimation errors after successive interference cancellation. To validate our experimental results, we compare them to the Cramer-Rao lower bounds for the estimation of channel parameters in case of superimposed signals.

关键词： Satellite communication Network coding Channel estimation expectation-maximization algorithms Cramer Rao Bounds

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Identification of core-periphery structure in networks

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Physical Review E 2015年第3期91卷 032803-032803页

作者： Xiao Zhang Travis Martin M. E. J. Newman Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Center for the Study of Complex Systems University of Michigan Ann Arbor Michigan 48109 USA

Many networks can be usefully decomposed into a dense core plus an outlying, loosely connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation-maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes, and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.

关键词： STATISTICAL physics expectation-maximization algorithms DECOMPOSITION (Mathematics) ERROR rates DISTRIBUTION (Probability theory)

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The EM Method in a Probabilistic Wavelet-Based MRI Denoising

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COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015年第1期2015卷 1-21页

作者： Martin-Fernandez, Marcos Villullas, Sergio Escuela Tecn Super Ingn Telecomun Lab Proc Imagen Valladolid 47011 Spain Univ Valladolid Fac Ciencias Dept Algebra Anal Matemat Geometria & Topol E-47011 Valladolid Spain

Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images.

关键词： expectation-maximization algorithms PROBABILITY theory WAVELETS (Mathematics) SIGNAL denoising MAGNETIC resonance imaging

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Adaptive Autoregressive Model for Reduction of Noise in SPECT

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COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015年第1期2015卷 1-9页

作者： Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti Oulu Univ Hosp OYS Dept Diagnost Radiol Div Nucl Med Oulu 90029 Finland Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland Joint Author Paijat Hame Social & Hlth Care Dept Clin Physiol & Nucl Med Lahti 15850 Finland

This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-ARmethod). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BWmethod). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot

关键词： AUTOREGRESSION (Statistics) NOISE control SINGLE-photon emission computed tomography IMAGE processing expectation-maximization algorithms IMAGE reconstruction

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A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

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SCIENTIFIC REPORTS 2015年第1期5卷 10576-10576页

作者： Lu, Qiongshi Hu, Yiming Sun, Jiehuan Cheng, Yuwei Cheung, Kei-Hoi Zhao, Hongyu Yale Univ Sch Publ Hlth Dept Biostat New Haven CT 06520 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT USA Yale Univ Sch Med Yale Ctr Med Informat New Haven CT USA Yale Univ Sch Med Dept Emergency Med New Haven CT USA VA Connecticut Healthcare Syst West Haven CT USA

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://***

关键词： HUMAN genome NON-coding DNA NON-coding RNA GENE expression expectation-maximization algorithms

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