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EM-ML algorithm for track initialization using possibly noninformative data

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2005年第3期41卷 1030-1048页

作者： Cai, J Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an F1 Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the, ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the IMM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection,,and computational load are illustrated using simulated scenarios as well.

关键词： algorithms ESTIMATION theory FIGHTER planes FALSE alarms expectation-maximization algorithms REFLECTION (Optics)

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Unsupervised statistical neural networks for model-based object recognition

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第11期45卷 2709-2718页

作者： Kumar, VP Manolakos, ES NORTHEASTERN UNIV DEPT ELECT & COMP ENGNBOSTONMA 02115

Statistical neural networks executing soft decision algorithms have been shown to be very effective in many classification problems. A neural network architecture is developed here that can perform unsupervised joint segmentation and labeling of objects in images. We propose the semi-parametric hierarchical mixture density (HMD) model as a tool for capturing the diversity of real world images and pose the object recognition problem as a maximum likelihood (ML) estimation of the HMD parameters. We apply the expectation-maximization (EM) algorithm for this purpose and utilize ideas and techniques from statistical physics to cast the problem as the minimization of a free energy function. We then proceed to regularize the solution thus obtained by adding smoothing terms to the objective function. The resulting recursive scheme for estimating the posterior probabilities of an object's presence in an image corresponds to an unsupervised feedback neural network architecture. We present here the results of experiments involving recognition of traffic signs in natural scenes using this technique.

关键词： expectation-maximization algorithms neural networks object recognition

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Convergence and Stability of Iteratively Re-weighted Least Squares algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第1期62卷 183-195页

作者： Ba, Demba Babadi, Behtash Purdon, Patrick L. Brown, Emery N. MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Massachusetts Gen Hosp Dept Anesthesia Crit Care & Pain Med Boston MA 02114 USA MIT Harvard MIT Div Hlth Sci & Technol Cambridge MA 02139 USA

In this paper, we study the theoretical properties of iteratively re-weighted least squares (IRLS) algorithms and their utility in sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between the IRLS algorithms and a class of expectation-maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribution. The EM formalism, as well as the connection to GSMs, allow us to establish that the IRLS algorithms minimize smooth versions of the l(nu) 'norms', for 0 < nu <= 1. We leverage EM theory to show that the limit points of the sequence of IRLS iterates are stationary points of the smooth "norm" minimization problem on the constraint set. We employ techniques from Compressive Sampling (CS) theory to show that the IRLS algorithm is stable, if the limit point of the iterates coincides with the global minimizer. We further characterize the convergence rate of the IRLS algorithm, which implies global linear convergence for nu = 1 and local super-linear convergence for 0 < nu < 1. We demonstrate our results via simulation experiments. The simplicity of IRLS, along with the theoretical guarantees provided in this contribution, make a compelling case for its adoption as a standard tool for sparse signal recovery.

关键词： Compressive sampling constrained maximum likelihood estimation Gaussian scale mixtures expectation-maximization algorithms mathematical programming

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Performance Comparison of Recent Imputation Methods for Classification Tasks over Binary Data

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APPLIED ARTIFICIAL INTELLIGENCE 2017年第1期31卷 1-22页

作者： Ghorbani, Soroosh Desmarais, Michel C. Polytech Montreal Dept Comp Engn 2900 Boul Edouard Montpetit Montreal PQ H3T 1J4 Canada

This paper evaluates the effect on the predictive accuracy of different models of two recently proposed imputation methods, namely missForest (MF) and Multiple Imputation based on expectation-maximization (MIEM), along with two other imputation methods: Sequential Hot-deck and Multiple Imputation based on Logistic Regression (MILR). Their effect is assessed over the classification accuracy of four different models, namely Tree Augmented Naive Bayes (TAN) which has received little attention, Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Experiments are conducted over fourteen binary datasets with large feature sets, and across a wide range of missing data rates (between 5 and 50%). The results from 10 fold cross-validations show that the performance of the imputation methods varies substantially between different classifiers and at different rates of missing values. The MIEM method is shown to generally give the best results for all the classifiers across all rates of missing data. While NB model does not benefit much from imputation compared to a no imputation baseline, LR and TAN are highly susceptible to gain from the imputation methods at higher rates of missing values. The results also show that MF works best with TAN, and Hot-deck degrades the predictive performance of SVM and NB models at high rates of missing values (over 30%). Detailed analysis of the imputation methods over the different datasets is reported. Implications of these findings on the choice of an imputation method are discussed.

关键词： expectation-maximization algorithms Logistic regression analysis Naive Bayes classification Support vector machines Radial basis functions

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Relevance feedback based on non-negative matrix factorisation for image retrieval

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 2006年第4期153卷 436-444页

作者： Liang, D. Yang, J. Chang, Y. Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

As a powerful tool for content-based image retrieval, many techniques have been proposed for relevance feedback. A non-negative matrix factorisation (NMF)-based relevance feedback approach is introduced. This approach uses a standard NMF algorithm to construct a reliable semantic space from a pool of relevant images based on a user's interactions, because the latent semantic space derived by NMF does not need to be orthogonal, and each image is guaranteed to take only non-negative values in all the latent semantic directions. It means that each axis in the space derived by NMF has a straightforward correspondence with each image semantic class. In addition, the hidden semantic features of the query and images in the database are extracted with an NMF-projecting algorithm. By memorising the feedback information provided by the user, the knowledge accumulated from past relevance interaction is used to update semantic space, which results in the semantic space being closer to the user's expectation. The experiments show that the proposed NMF-based relevance feedback approach performs better than other relevance feedback approaches.

关键词： INFORMATION retrieval PRINCIPAL components analysis FACTOR analysis expectation-maximization algorithms IMAGE processing IMAGING systems

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Adults Use Cross-Situational Statistics for Word Learning in a Conservative Way

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2020年第2期12卷 232-242页

作者： Aussems, Suzanne Vogt, Paul Univ Warwick Dept Psychol Coventry CV4 7AL W Midlands England Tilburg Univ Dept Cognit Sci & Artificial Intelligence NL-5000 Tilburg Netherlands

This paper examined how much adults rely on cross-situational information in word learning by comparing their gaze behavior in a word learning task with models of four learning strategies. We manipulated the input type of situations (consecutive versus interleaved) and the co-occurrence frequencies between words and objects so that adult learners could infer correct word-object mappings based on cross-situational information. There are two key findings. First, an exposure-by-exposure analysis of gaze behavior during the word learning procedure revealed that most participants collected sufficient cross-situational information before they developed a preference for one particular word-object mapping, with consecutive as well as interleaved situations. Second, a classification approach in which individual gaze behavior was attributed to different word learning strategies showed that participants relied mostly on a conservative cross-situational learning (XSL) strategy, compared to Associative XSL, Propose-but-Verify, and Random strategies. Adults relied on Conservative XSL when presented with consecutive and interleaved situations, but they shifted toward Associative XSL when presented with interleaved situations.

关键词： Training Task analysis Object recognition Uncertainty Gaze tracking expectation-maximization algorithms Psychology Adults cross-situational word learning (XSL) expectation-maximization (EM) algorithm eye tracking Propose-but-Verify (PbV)

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Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2017年第6期354卷 2477-2499页

作者： Zhang, Zhengxin Hu, Changhua Si, Xiaosheng Zhang, Jianxun Zheng, Jianfei Xian Inst High Tech Xian 710025 Shaanxi Provinc Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Models with random-effects are generally used in the field of degradation modeling and remaining useful life (RUL) estimation for describing unit-to-unit variability. The wide employment of parameters, which is assumed to be subjected to normal distribution to capture this variability, may disaccord with actual industrial conditions, and will introduce misspecifications. Such misspecification can affect the accuracy of RUL estimation and the subsequent inference results. In this paper, we propose a degradation model with flexible random-effects, which makes it flexible to choose distributions to portray the unit-to-unit variability according to the available information. To do so, the mixture of normal distributions, as a distribution describing random-effects, is incorporated into a class of diffusion process based degradation models whose drift coefficient is a linear combination of some time-dependent functions with known forms. The combination coefficients of each function are treated as random variables drawn from the mixture of normal distributions. An analytical approximated probability density function (PDF) of the RUL is derived under the concept of first passage time (FPT). To identify the model parameters, a framework for parameter estimation is presented based on stochastic expectation maximization (SEM) algorithm. Finally, simulation studies are provided to demonstrate the superiority of the normal mixture over the individual normal distribution for describing random effects in RUL estimation. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： STOCHASTIC models RANDOM effects model DIFFUSION processes PROBABILITY density function expectation-maximization algorithms PARAMETER estimation

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Diagnostics for skew-normal nonlinear regression models with AR(1) errors

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 4403-4416页

作者： Xie, Feng-Chang Lin, Jin-Guan Wei, Bo-Cheng Southeast Univ Dept Math Nanjing 210096 Peoples R China Nanjing Agr Univ Dept Math Nanjing 210095 Peoples R China

In this article, we consider some diagnostics for skew-normal nonlinear regression models with AR(1) errors which provide a useful extension of the normal regression models. The estimation of the parameters in the models is studied based on the EM algorithm. Meanwhile, several score tests are presented for testing the homogeneity of the scale parameter and/or significance of autocorrelation in skew-normal nonlinear regression models. The properties of score tests are investigated through Monte Carlo simulations. The test methods are illustrated with two numerical examples. (C) 2009 Elsevier B.V. All rights reserved.

关键词： DIAGNOSIS NONLINEAR models (Statistics) REGRESSION analysis MEASUREMENT errors expectation-maximization algorithms AUTOCORRELATION (Statistics) MONTE Carlo method SIMULATION methods & models NUMERICAL analysis

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A BAYESIAN-ESTIMATION APPROACH FOR SPEECH ENHANCEMENT USING HIDDEN MARKOV-MODELS

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第4期40卷 725-735页

作者： EPHRAIM, Y Speech Research Department AT and T Bell Laboratories Inc. Murray Hill NJ USA

A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed. In particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMM's) for the clean signal and the noise process. It is shown that the MMSE estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal (pairs of states of the models for the signal and noise), where the weights equal the posterior probabilities of the composite states given the noisy signal. The estimation of several spectral functions of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered. A gain-adapted MAP estimator is developed using the expectation-maximization (EM) algorithm. In this approach an HMM for gain-normalized clean signals is estimated from the training data, and the signal and its gain contour are estimated from the noisy signal using the MAP estimation approach. The theoretical performance of the MMSE estimator is discussed, and convergence of the MAP estimator is proved. Both the MMSE and MAP estimators are tested in enhancing speech signals degraded by white Gaussian noise at input signal-to-noise ratio of from 5 to 20 dB.

关键词： Bayesian methods Speech enhancement Degradation Signal processing Hidden Markov models State estimation Additive noise Mean square error methods Phase estimation expectation-maximization algorithms

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Multiple model approach to linear parameter varying time-delay system identification with EM algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2014年第12期351卷 5565-5581页

作者： Yang, Xianqiang Gao, Huijun Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China

This paper is concerned with the identification problem of linear parameter varying (LPV) time-delay systems. Due to inherent nonlinearity, the industrial processes are often approximately described by an LPV model constructed by synthesizing multiple local models. Time-delay is commonly experienced in industrial processes and it can be parameter varying or constant in the process model. The multiple model identification of LPV systems with parameter varying or constant time-delay is formulated in the scheme of the expectation-maximization (EM) algorithm and the parameter varying property and the time-delay property of the process are handled simultaneously. The irrigation channel example and high purity distillation column example are used to present the effectiveness of the proposed method. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： expectation-maximization algorithms LINEAR systems TIME delay systems PARAMETERS (Statistics) IDENTIFICATION (Statistics) MANUFACTURING processes NONLINEAR theories

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