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12th Symposium on Ocean Electronics (SYMPOL)

作者： Philip, Dibu John Anand, G. V. Kumar, N. Suresh Rajagiri Sch Engn & Technol Kochi 682039 Kerala India PES Inst Technol Bangalore 560085 Karnataka India Naval Phys & OceanGraph Lab Kochi 682021 Kerala India

ISBN: (纸本)9789380095455

A computationally efficient method of three-dimensional (3-D) localization of underwater acoustic sources using an acoustic vector sensor (AVS) array is presented in this paper. Noise is modeled as a Gaussian mixture (GM), and an expectation maximization (EM) algorithm is used to estimate all the unknown parameters. The proposed method consists of an initialization stage followed by iterative updates which converge in a small number of iterations. Performance of the proposed method is better than that of existing methods, and performance of the AVS array is much better than that of the acoustic pressure sensor (APS) array.

关键词： Acoustic vector sensor expectation maximization algorithm Maximum likelihood estimation Non-Gaussian noise Three-dimensional localization

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Centroid based clustering of high throughput sequencing reads based on n-mer counts

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BMC BIOINFORMATICS 2013年第1期14卷 1-21页

作者： Solovyov, Alexander Lipkin, W. Ian Columbia Univ Ctr Infect & Immun New York NY 10032 USA

Background: Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. Results: We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://***/luscinius/afcluster. Conclusions: We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.

关键词： expectation maximization Recall Rate expectation maximization algorithm Word Count Consensus Cluster

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Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows

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BMC BIOINFORMATICS 2013年第Sup9期14卷 S2-S2页

作者： Skums, Pavel Mancuso, Nicholas Artyomenko, Alexander Tork, Bassam Mandoiu, Ion Khudyakov, Yury Zelikovsky, Alex Ctr Dis Control & Prevent Lab Mol Epidemiol & Bioinformat Div Viral Hepatitis Atlanta GA 30333 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

Background: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. Results: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. Conclusions: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://***/vira.h

关键词： Root Mean Square Error Positive Predict Value expectation maximization expectation maximization algorithm Viral Population

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Nonlinear system identification with multiple and correlated scheduling variables *

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IFAC Proceedings Volumes 2013年第32期46卷 319-324页

作者： Lei Chen Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 P.R. China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

This paper is concerned with identification of nonlinear systems with multiple and correlated scheduling variables. Multiple auto regressive exogenous (ARX) models are identified on different process operating conditions, and a normalized exponential function as the probability density function associated with each of the local ARX models taking effect is then used to combine all the local models to represent the complete dynamics of a nonlinear system. The parameters of the local ARX models and the exponential functions are estimated simultaneously under the framework of the expectation maximization (EM) algorithm. A numerical example is applied to demonstrate the proposed identification method.

关键词： System identification Nonlinear process Multiple models expectation maximization algorithm Multiple scheduling variables

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

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IEEE International Conference on Computational Cybernetics

作者： Taher Ben Arab Mourad Zribi Afif Masmoudi Laboratoire d'Informatique Signal et Image de la Cote d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Laboratoire d'Informatique Signal et Image de la C?te d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Tunisie.

ISBN: (纸本)9781479900626

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a K-Means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a K-means clustering algorithm and expectation-maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta type-II attributes a specific distribution type according to different parameters. Different adapted algorithms (namely K-Means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II expectation maximization algorithm K-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Elsa DUPRAZ Aline ROUMY Michel KIEFFER L2S - CNRS - SUPELEC - Univ Paris-Sud 91192 Gif-sur-Yvette France INRIA Campus de Beaulieu 35042 Rennes France

ISBN: (纸本)9781479903573

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and non-binary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the LDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate but known variance.

关键词： Source coding with side information Rate-distortion Low Density Parity Check codes expectation maximization algorithm low density parity check codes expectation-maximisation algorithm Rate distortion labeling scheme Gauss scalar quantization lossy source coding binarization

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Building a term suggestion and ranking system based on a probabilistic analysis model and a semantic analysis graph

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DECISION SUPPORT SYSTEMS 2012年第1期53卷 257-266页

作者： Chen, Lin-Chih Natl Dong Hwa Univ Dept Informat Management Shoufeng 97401 Hualien Taiwan

Term suggestion is a kind of information retrieval technique that attempts to suggest relevant terms to help users formulate more effective queries and reduce unnecessary search steps. In this paper, we apply two semantic analysis methods, the probabilistic analysis model and semantic analysis graph, to design a term suggestion system that can effectively deal with the problems of synonymy and polysemy. The main contributions of this paper are the following. First, we apply two semantic analysis methods to design a high-performance term suggestion system. Second, we design an intelligent mechanism that can effectively balance cost and performance to minimize the number of iterations required for our system. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Probabilistic analysis model Semantic analysis graph Probability parameters expectation maximization algorithm Euclidean distance

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Semi-supervised learning via manifold regularization

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The Journal of China Universities of Posts and Telecommunications 2012年第6期19卷 79-88页

作者： MAO Yu ZHOU Yan-quan LI Rui-fan WANG Xiao-jie ZHONG Yi-xin School of Telecommunication Engineering Beijing University of Posts and Telecommunications

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

关键词： manifold regularization semi-supervised learning transductive learning expectation maximization algorithm classification text categorization

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Improved Parameter Estimation with Threshold Adaptation of Cognitive Local Sensors

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2012年第5期14卷 471-480页

作者： Seol, Dae-Young Lim, Hyoung-Jin Song, Moon-Gun Im, Gi-Hong Pohang Univ Sci & Technol Dept Elect & Elect Engn Pohang 790784 South Korea

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

关键词： Cognitive radio cooperative spectrum sensing expectation maximization algorithm likelihood ratio test unsupervised learning

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FUNCTIONAL DYNAMIC FACTOR MODELS WITH APPLICATION TO YIELD CURVE FORECASTING

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ANNALS OF APPLIED STATISTICS 2012年第3期6卷 870-894页

作者： Hays, Spencer Shen, Haipeng Huang, Jianhua Z. Pacific NW Natl Lab Natl Secur Directorate Richland WA 99354 USA Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27599 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation-maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.

关键词： Functional data analysis expectation maximization algorithm natural cubic splines cross-validation roughness penalty

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