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Mixture modeling with applications in schizophrenia research

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第7期53卷 2563-2572页

作者： Wu, Qiang Sampson, Allan R. E Carolina Univ Dept Biostat Greenville NC 27858 USA Univ Pittsburgh Dept Stat Pittsburgh PA 15260 USA

Finite mixture modeling, together with the EM algorithm, have been widely used in clustering analysis. Under such methods, the unknown group membership is usually treated as missing data. When the "complete data" (log-)likelihood function does not have an explicit solution, the simplicity of the EM algorithm breaks down. Authors, including Rai and Matthews [Rai, S.N., Matthews, D.E., 1993. Improving the em algorithm. Biometrics 49, 587-591], Lange [Lange, K., 1995a. A gradient algorithm locally equivalent to the em algorithm. Journal of the Royal Statistical Society B 57(2) 425-437], and Titterington [Titterington, D.M., 1984. Recursive parameter estimation using incomplete data. journal of the Royal Statistical Society B. 46, 257-267] developed modified algorithms therefore. As motivated by research in a large neurobiological project, we propose in this paper a new variant of such modifications and show that it is self-consistent. Moreover, simulations are conducted to demonstrate that the new variant converges faster than its predecessors. (C) 2008 Elsevier B.V. All rights reserved.

关键词： SCHIZOPHRENIA RESEARCH MATHEMATICAL models expectation-maximization algorithms CLUSTER analysis (Statistics) ESTIMATION theory MATHEMATICAL statistics

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Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

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IEEE ACCESS 2020年 8卷 789-801页

作者： Li, Zhi Yang, Liqun Li, Zhoujun Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China

Privacy has become a major concern in data mining as it is utilized in many important applications. Distributed privacy-preserving data mining (DPPDM) is one of the techniques to address this concern, which focuses on protecting private information of members in distributed systems during data mining. As DPPDM is widely discussed in recent works, the semi-supervised manner of learning still draws less attention in this field. In this paper, a mixture-model-based semi-supervised DPPDM method is proposed. By introducing our method, a site in a distributed system is able to initiate a learning process using labeled data of its own and unlabeled data from all the sites. During the process, no individual data of any site is revealed to others, no information about data can be traced back to any specific site, and only the initiating site learns the result. We propose a parameter-masking privacy-preserving expectation-maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm as the two main steps of our method. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.

关键词： Data privacy distributed computing expectation-maximization algorithms graph theory semisupervised learning

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Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2011年第21期32卷 6569-6586页

作者： Dlamini, Wisdom M. Swaziland Natl Trust Commiss Lobamba H107 Eswatini

This article describes the use of a Bayesian network (BN) for the classification of land cover from satellite imagery in northern Swaziland. The main objective of this work was to apply and evaluate the efficacy of a BN for land-cover classification using gap-filled and terrain-corrected Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery acquired on 15 May 2007. The posterior probabilities (parameters) were estimated using the expectation-maximization (EM) and conjugate gradient descent (CGD) algorithms. A comparison of the results obtained from the algorithms indicates similar and excellent overall classification accuracies of 93.01%, and kappa coefficient values of 0.9143. The main result obtained in this study is that both algorithms considered here provide relatively similar and accurate solutions for the classification of the multispectral image although the EM algorithm is marginally competitive relative to CGD algorithm when measured in terms of the Brier score and the logarithmic loss.

关键词： REMOTE-sensing images BAYESIAN analysis expectation-maximization algorithms CONJUGATE gradient methods

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PARALLEL algorithms FOR MAXIMUM A-POSTERIORI ESTIMATION OF SPIN-DENSITY AND SPIN-SPIN DECAY IN MAGNETIC-RESONANCE-IMAGING

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1995年第2期14卷 362-373页

作者： SCHAEWE, TJ MILLER, MI WASHINGTON UNIV MALLINCKRODT INST RADIOLDEPT ELECT ENGNST LOUISMO 63130 WASHINGTON UNIV INST BIOMED COMPST LOUISMO 63130

A maximum a posteriori (MAP) algorithm is presented for the estimation of spin-density and spin-spin decay distributions from frequency and phase-encoded magnetic resonance imaging data. Linear spatial localization gradients are assumed: the y-encode gradient applied during the phase preparation time of duration tau before measurement collection, and the x-encode gradient applied during the full data collection time t greater than or equal to 0, The MRT signal model developed in [22] is used in which a signal resulting from M phase encodes (rows) and N frequency encode dimensions (columns) is modeled as a superposition of MN sine-modulated exponentially decaying sinusoids with unknown spin-density and spin-spin decay parameters, The nonlinear least-squares MAP estimate of the spin density and spin-spin decay distributions solves for the 2MN spin-density and decay parameters minimizing the squared-error between the measured data and the sine-modulated exponentially decay signal model using an iterative expectation-maximization algorithm. A covariance diagonalizing transformation is derived which decouples the joint estimation of MN sinusoids into M separate N sinusoid optimizations, yielding an order of magnitude speed up in convergence, The MAP solutions are demonstrated to deliver a decrease in standard deviation of image parameter estimates on brain phantom data of greater than a factor of two over Fourier-based estimators of the spin density and spin-spin decay distributions. A parallel processor implementation is demonstrated which maps the N sinusoid coupled minimization to separate individual simple minimizations, one for each processor.

关键词： Parallel algorithms Maximum a posteriori estimation Frequency estimation Magnetic resonance imaging Phase estimation Phase measurement Time measurement Density measurement expectation-maximization algorithms Yield estimation

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Quantize and Forward Cooperative Communication: Channel Parameter Estimation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第3期11卷 1167-1179页

作者： Avram, Iancu Aerts, Nico Bruneel, Herwig Moeneclaey, Marc Univ Ghent Dept Telecommun & Informat Proc TELIN Fac Engn B-9000 Ghent Belgium Univ Ghent Dept Telecommun & Informat Proc SMACS Res Grp B-9000 Ghent Belgium

Cooperative communication systems can effectively be used to combat fading. A cooperative protocol that can be used with half-duplex terminals is the Quantize and Forward (QF) protocol, in which the relay quantizes the information received from the source before forwarding it to the destination. While the outage behavior and error performance of the QF protocol have been investigated extensively, only few research has been performed on the effects of channel parameter estimation. In this contribution, estimates are derived for all communication channels involved, while keeping the complexity of the relay terminal unaltered. First, channel estimates are calculated using known pilot symbols sent by the source and relay. Thereafter, these pilot-based channel estimates are refined using a code-aided expectation maximization (EM) approach, yielding an error performance that is very close to that of a system in which the channel parameters are assumed to be known.

关键词： Cooperative systems wireless communication channel estimation expectation-maximization algorithms maximum likelihood estimation

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Identification of continuous-time state-space models from non-uniform fast-sampled data

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IET CONTROL THEORY AND APPLICATIONS 2011年第7期5卷 842-855页

作者： Yuz, J. I. Alfaro, J. Agueero, J. C. Goodwin, G. C. UTFSM Dept Elect Engn Valparaiso Chile Univ Newcastle Ctr Complex Dynam Syst & Control CDSC Callaghan NSW 2308 Australia

In this study, we apply the expectation-maximisation (EM) algorithm to identify continuous-time state-space models from non-uniformly fast-sampled data. The sampling intervals are assumed to be small and uniformly bounded. The authors use a parameterisation of the sampled-data model in incremental form in order to modify the standard formulation of the EM algorithm for discrete-time models. The parameters of the incremental model converge to the parameter of the continuous-time system description as the sampling period goes to zero. The benefits of the proposed algorithm are successfully demonstrated via simulation studies.

关键词： CONTINUOUS-time filters STATE-space methods SAMPLING (Statistics) expectation-maximization algorithms INTERVAL analysis (Mathematics) DISCRETE-time systems SIMULATION methods & models

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A two-parameter lifetime distribution with decreasing failure rate

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第8期52卷 3889-3901页

作者： Tahmasbi, Rasool Rezaei, Sadegh Amirkabir Univ Technol Dept Stat Tehran Iran Shahid Chamran Univ Ahvaz Dept Stat Ahvaz Iran

In this paper, a new two-parameter lifetime distribution with decreasing failure rate is introduced. Various properties of the proposed distribution are discussed. The estimation of the parameters attained by the EM algorithm and their asymptotic variances and covariances are obtained. In order to assess the accuracy of the approximation of variances and covariances of the maximum likelihood estimators, simulation studies are performed and experimental results are illustrated based on real data sets. (c) 2007 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithms algorithms ESTIMATION theory MATHEMATICAL statistics

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Solving Time-Domain Audio Inverse Problem Using Nonnegative Tensor Factorization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第21期66卷 5604-5617页

作者： Bilen, Cagdas Ozerov, Alexey Perez, Patrick Audio Analyt Ltd Cambridge CB5 8AB England Technicolor F-35576 Cesson Sevigne France Valeo Ai F-75017 Paris France

Nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) are important tools for modeling nonnegative data, which gained increasing popularity in various fields, a significant one of which is audio processing. However, there are still many problems in audio processing, for which the NMF (or NTF) model has not been successfully utilized. In this paper, we propose a new algorithm based on the NMF (and NTF) in the short-time Fourier domain for solving a large class of audio inverse problems with missing or corrupted time-domain samples. The proposed approach overcomes the difficulty of employing a model in the frequency domain to recover time-domain samples with the help of probabilistic modeling. Its performance is demonstrated for the following applications: audio declipping and declicking (never solved with NMF/NTF modeling prior to this paper);joint audio declipping/declicking and source separation (never solved with NMF/NTF modeling or any other method prior to this paper);and compressive sampling recovery and compressive sampling-based informed source separation (an extremely low complexity encoding scheme that is possible with the proposed approach and has never been proposed prior to this paper).

关键词： Signal reconstruction signal restoration maximum likelihood estimation maximum a posteriori estimation expectation-maximization algorithms iterative algorithms

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A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第2期66卷 294-308页

作者： Al-Shoukairi, Maher Schniter, Philip Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into expectation-maximization-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to arbitrary measurement matrix A than the standard damped GAMP algorithm while being much lower complexity than the standard SBL algorithm. We then extend the approach from the single measurement vector case to the temporally correlated multiple measurement vector case, leading to the GGAMP-TSBL algorithm. We verify the robustness and computational advantages of the proposed algorithms through numerical experiments.

关键词： Compressed sensing approximate message passing (AMP) sparse Bayesian learning (SBL) expectation-maximization algorithms Gaussian scale mixture multiple measurement vectors (MMV)

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Online updating appearance generative mixture model for meanshift tracking

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MACHINE VISION AND APPLICATIONS 2009年第3期20卷 163-173页

作者： Tu, Jilin Tao, Hai Huang, Thomas Univ Illinois Urbana & Champaign Dept Elect & Comp Engn Urbana IL 61801 USA Univ Calif Santa Cruz Dept Comp Engn Santa Cruz CA 95064 USA

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses. The online version of this article (doi:10.1007/s00138-007-0115-x) contains supplementary material, which is available to authorized users.

关键词： expectation-maximization algorithms ARITHMETIC -- Foundations GRAPHICAL modeling (Statistics) MULTIVARIATE analysis -- Graphic methods ALGEBRA

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