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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Jezierska, Anna Talbot, Hugues Chaux, Caroline Pesquet, Jean-Christophe Engler, Gilbert Univ Paris Est CNRS LIGM UMR 8049 F-77454 Marne La Vallee France INRA IBSV Unit F-06903 Sophia Antipolis France

ISBN: (纸本)9781457718588

In this paper, we present a new fully automatic approach for noise parameter estimation in the context of fluorescence imaging systems. In particular, we address the problem of Poisson-Gaussian noise modeling in the nonstationary case. In microscopy practice, the nonstationarity is due to the photobleaching effect. The proposed method consists of an adequate moment based initialization followed by expectation-maximization iterations. This approach is shown to provide reliable estimates of the mean and the variance of the Gaussian noise and of the scale parameter of Poisson noise, as well as of the photobleaching rates. The algorithm performance is demonstrated on both synthetic and real macro confocal laser scanning microscope image sequences.

关键词： Noise identification confocal imaging systems calibration fluorescence photobleaching expectation-maximization algorithm image calibration

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Combining Machine Learning and Information Retrieval Techniques for Software Clustering

Combining Machine Learning and Information Retrieval Techniq...

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1st International Workshop on Eternal Systems (EternalS 2011)

作者： Corazza, Anna Di Martino, Sergio Maggio, Valerio Scanniello, Giuseppe Univ Naples Federico II Naples Italy Univ Basilicata Potenza Italy

ISBN: (纸本)9783642280320

In the field of Software Maintenance the definition of effective approaches to partition a software system into meaningful subsystems is a longstanding and relevant research topic. These techniques are very important as they can significantly support a Maintainer in his/her tasks by grouping related entities of a large system into smaller and easier to comprehend subsystems. In this paper we investigate the effectiveness of combining information retrieval and machine learning techniques in order to exploit the lexical information provided by programmers for software clustering. In particular, differently from any related work, we employ indexing techniques to explore the contribution of the combined use of six different dictionaries, corresponding to the six parts of the source code where programmers introduce lexical information, namely: class, attribute, method and parameter names, comments, and source code statements. Moreover their relevance is estimated on the basis of the project characteristics, by applying a machine learning approach based on a probabilistic model and on the expectation-maximization algorithm. To group source files accordingly, two clustering algorithms have been compared, i.e. the K-Medoids and the Group Average Agglomerative Clustering, and the investigation has been conducted on a dataset of 9 open source Java software systems.

关键词： expectation-maximization algorithm Information Retrieval Probabilistic Model Remodularization Software Clustering Software Evolution

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A class of bivariate Erlang distributions and ruin probabilities in multivariate risk models

A class of bivariate Erlang distributions and ruin probabili...

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作者： Groparu-Cojocaru, Ionica Université de Montréal

学位级别：doctor

Nous y introduisons une nouvelle classe de distributions bivariées de type Marshall-Olkin, la distribution Erlang bivariée. La transformée de Laplace, les moments et les densités conditionnelles y sont obtenus. Les applications potentielles en assurance-vie et en finance sont prises en considération. Les estimateurs du maximum de vraisemblance des paramètres sont calculés par l''algorithme Espérance-Maximisation. Ensuite, notre projet de recherche est consacré à l''étude des processus de risque multivariés, qui peuvent être utiles dans l''étude des problèmes de la ruine des compagnies d''assurance avec des classes dépendantes. Nous appliquons les résultats de la théorie des processus de Markov déterministes par morceaux afin d''obtenir les martingales exponentielles, nécessaires pour établir des bornes supérieures calculables pour la probabilité de ruine, dont les expressions sont intraitables.%%%%In this contribution, we introduce a new class of bivariate distributions of Marshall-Olkin type, called bivariate Erlang distributions. The Laplace transform, product moments and conditional densities are derived. Potential applications of bivariate Erlang distributions in life insurance and finance are considered. Further, our research project is devoted to the study of multivariate risk processes, which may be useful in analyzing ruin problems for insurance companies with a portfolio of dependent classes of business. We apply results from the theory of piecewise deterministic Markov processes in order to derive exponential martingales needed to establish computable upper bounds of the ruin probabilities, as their exact expressions are intractable

关键词： Education - Mathematics / éducation - Mathématiques (UMI 0280) Distribution Erlang algorithme Espérance-Maximisation Processus de Markov déterministes par morceaux Modèle de risque multivarié Probabilité de ruine Modèle de Poisson avec chocs communs Processus de renouvellement Copules Erlang distribution expectation-maximization algorithm Piecewise deterministic Markov processes Multivariate risk model Ruin probability Poisson model with common shocks Renewal processes Copulas

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Speed-up for the expectation-maximization algorithm for clustering categorical data

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JOURNAL OF GLOBAL OPTIMIZATION 2007年第4期37卷 513-525页

作者： Jollois, F. -X. Nadif, M. Univ Paris 05 CRIP5 F-75270 Paris 06 France Univ Paul Verlaine Metz LITA UFR MIM F-57045 Metz 1 France

In model-based cluster analysis, the expectation-maximization (EM) algorithm has a number of desirable properties, but in some situations, this algorithm can be slow to converge. Some variants are proposed to speed-up EM in reducing the time spent in the E-step, in the case of Gaussian mixture. The main aims of such methods is first to speed-up convergence of EM, and second to yield same results (or not so far) than EM itself. In this paper, we compare these methods from categorical data, with the latent class model, and we propose a new variant that sustains better results on synthetic and real data sets, in terms of convergence speed-up and number of misclassified objects.

关键词： mixture model expectation-maximization algorithm clustering acceleration categorical data

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An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2007年第7期54卷 1191-1198页

作者： Khan, Mohammad Emtiyaz Dutt, Deshpande Narayana Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

We consider the problem of event-related desynchronization (ERD) estimation. In existing approaches, model parameters are usually found manually through experimentation, a tedious task that often leads to suboptimal estimates. We propose an expectation-maximization (EM) algorithm for model parameter estimation that is fully automatic and gives optimal estimates. Further, we apply a Kalman smoother to obtain ERD estimates. Results show that the EM algorithm significantly improves the performance of the Kalman smoother. Application of the proposed approach to the motor-imagery EEG data shows that useful ERD patterns can be obtained even without careful selection of frequency bands.

关键词： event-related desynchronization expectation-maximization algorithm Kalman smoother

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ANDERSON ACCELERATION FOR FIXED-POINT ITERATIONS

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SIAM JOURNAL ON NUMERICAL ANALYSIS 2011年第4期49卷 1715-1735页

作者： Walker, Homer F. Ni, Peng Worcester Polytech Inst Dept Math Sci Worcester MA 01609 USA

This paper concerns an acceleration method for fixed-point iterations that originated in work of D. G. Anderson [J. Assoc. Comput. Mach., 12 (1965), pp. 547-560], which we accordingly call Anderson acceleration here. This method has enjoyed considerable success and wide usage in electronic structure computations, where it is known as Anderson mixing;however, it seems to have been untried or underexploited in many other important applications. Moreover, while other acceleration methods have been extensively studied by the mathematics and numerical analysis communities, this method has received relatively little attention from these communities over the years. A recent paper by H. Fang and Y. Saad [Numer. Linear Algebra Appl., 16 (2009), pp. 197-221] has clarified a remarkable relationship of Anderson acceleration to quasi-Newton (secant updating) methods and extended it to define a broader Anderson family of acceleration methods. In this paper, our goals are to shed additional light on Anderson acceleration and to draw further attention to its usefulness as a general tool. We first show that, on linear problems, Anderson acceleration without truncation is "essentially equivalent" in a certain sense to the generalized minimal residual (GMRES) method. We also show that the Type 1 variant in the Fang-Saad Anderson family is similarly essentially equivalent to the Arnoldi (full orthogonalization) method. We then discuss practical considerations for implementing Anderson acceleration and illustrate its performance through numerical experiments involving a variety of applications.

关键词： acceleration methods fixed-point iterations generalized minimal residual method Arnoldi (full orthogonalization) method iterative methods expectation-maximization algorithm mixture densities alternating least-squares nonnegative matrix factorization domain decomposition

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An expectation-maximization-based Approach to the Relative Grid-Locking Problem

An Expectation-Maximization-based Approach to the Relative G...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Fortunati, Stefano Gini, Fulvio Greco, Maria S. Farina, Alfonso Graziano, Antonio Giompapa, Sofia Univ Pisa Dept Ingn Informaz Pisa Italy SELEX Sistemi Integrati Rome Italy

ISBN: (纸本)9781467307536

An important prerequisite for successful multisensory integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we take into account all registration errors involved in the grid-locking problem. An EM-based estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramer-Rao lower bound (HCRLB).

关键词： Multi-sensor system sensor registration bias errors expectation-maximization algorithm HCRLB

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Moments of Mixture Periodic Autoregressive Models

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2011年第22期40卷 3937-3947页

作者： Bentarzi, M. Merzougui, M. Univ Sci & Technol USTHB Algiers Algeria Univ MHamed Bouguera Boumerdes Algeria

This article deals with the study of some properties of a mixture periodically correlated autoregressive (MPAR(S)) time series model, which extends the mixture time invariant parameter autoregressive (MAR) model, that has recently received a considerable interest from many economic time series analysts, to mixture periodic parameter autoregressive model. The aim behind this extension is to make the model able to capture, in addition to all features captured by the classical MAR model, the periodicity feature exhibited by the autocovariance structure of many encountered financial and environmental time series with eventual multimodal distributions. Our main contribution here is obtaining of the second moment periodically stationary condition for a MPAR(S) (K;2, ... , 2) model, furthermore the closed-form of the second moment is obtained.

关键词： expectation-maximization algorithm Mixture periodic autoregressive models Periodic autoregressive models Periodically correlated process

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Sparse Variational Bayesian SAGE algorithm With Application to the Estimation of Multipath Wireless Channels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第8期59卷 3609-3623页

作者： Shutin, Dmitriy Fleury, Bernard H. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Aalborg Univ Dept Elect Syst Sect Nav & Commun DK-9220 Aalborg Denmark

In this paper, we develop a sparse variational Bayesian (VB) extension of the space-alternating generalized expectation-maximization (SAGE) algorithm for the high resolution estimation of the parameters of relevant multipath components in the response of frequency and spatially selective wireless channels. The application context of the algorithm considered in this contribution is parameter estimation from channel sounding measurements for radio channel modeling purpose. The new sparse VB-SAGE algorithm extends the classical SAGE algorithm in two respects: i) by monotonically minimizing the variational free energy, distributions of the multipath component parameters can be obtained instead of parameter point estimates and ii) the estimation of the number of relevant multipath components and the estimation of the component parameters are implemented jointly. The sparsity is achieved by defining parametric sparsity priors for the weights of the multipath components. We revisit the Gaussian sparsity priors within the sparse VB-SAGE framework and extend the results by considering Laplace priors. The structure of the VB-SAGE algorithm allows for an analytical stability analysis of the update expression for the sparsity parameters. This analysis leads to fast, computationally simple, yet powerful, adaptive selection criteria applied to the single multipath component considered at each iteration. The selection criteria are adjusted on a per-component-SNR basis to better account for model mismatches, e. g., diffuse scattering, calibration and discretization errors, allowing for a robust extraction of the relevant multipath components. The performance of the sparse VB-SAGE algorithm and its advantages over conventional channel estimation methods are demonstrated in synthetic single-input-multiple-output (SIMO) time-invariant channels. The algorithm is also applied to real measurement data in a multiple-input-multiple-output (MIMO) time-invariant context.

关键词： expectation-maximization algorithm MIMO multipath channels SAGE algorithm variational Bayesian methods

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BICM Decoding of Jammed OFDM Transmissions Using the EM algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2011年第9期10卷 2800-2806页

作者： Sanguinetti, Luca Morelli, Michele Poor, H. Vincent Univ Pisa Dept Informat Engn I-56126 Pisa Italy Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Wireless communications over unlicensed frequency bands are expected to suffer from significant co-channel interference, which inevitably limits the error-rate performance. In this letter, an orthogonal frequency-division multiplexing system employing bit-interleaved coded-modulation is considered and the problem of reliable decoding in the presence of unknown narrowband interference (NBI) is addressed. The proposed solution operates in an iterative fashion according to the expectation-maximization algorithm and is derived by modeling the interference power on each subcarrier as a random variable with an inverse gamma distribution. Computer simulations indicate that the resulting scheme is effective against NBI and, compared to existing alternatives, it achieves a better trade-off in terms of error rate performance, computational complexity and system overhead.

关键词： OFDM bit-interleaved coded-modulation expectation-maximization algorithm narrowband interference

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