A novel and efficient mixture model fitting technique, called penalized minimum matching distance-guided expectation-maximization (em) algorithm, is proposed. Penalized minimum matching distance is used to find the nu...
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A novel and efficient mixture model fitting technique, called penalized minimum matching distance-guided expectation-maximization (em) algorithm, is proposed. Penalized minimum matching distance is used to find the number of mixture components very accurately. We illustrate the excellent performance of the penalized minimum matching distance-guided em algorithm with experiments involving Gaussian mixtures. (c) 2005 Elsevier GmbH. All fights reserved.
作者：
Tsai, AWells, WMWarfield, SKWillsky, ASHarvard Univ
Sch Med Brigham & Womens Hosp Dept Radiol Boston MA 02115 USA MIT
Informat & Decis Syst Lab Cambridge MA 02139 USA MIT
Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Harvard Univ
Sch Med Boston Childrens Hosp Dept Radiol Boston MA 02115 USA
In this paper, we propose an expectation-maximization (em) approach to separate a shape database into different shape classes, while simultaneously estimating the shape contours that best exemplify each of the differe...
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In this paper, we propose an expectation-maximization (em) approach to separate a shape database into different shape classes, while simultaneously estimating the shape contours that best exemplify each of the different shape classes. We begin our formulation by employing the level set function as the shape descriptor. Next, for each shape class we assume that there exists an unknown underlying level set function whose zero level set describes the contour that best represents the shapes within that shape class. The level set function for each example shape in the database is modeled as a noisy measurement of the appropriate shape class's unknown underlying level set function. Based on this measurement model and the judicious introduction of the class labels as the hidden data, our em formulation calculates the labels for shape classification and estimates the shape contours that best typify the different shape classes. This resulting iterative algorithm is computationally efficient, simple, and accurate. We demonstrate the utility and performance of this algorithm by applying it to two medical applications. (c) 2005 Elsevier B.V. All rights reserved.
The application of the Bayesian Structural em algorithm to learn Bayesian networks (BNs) for clustering implies a search over the space of BN structures alternating between two steps: an optimization of the BN paramet...
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The application of the Bayesian Structural em algorithm to learn Bayesian networks (BNs) for clustering implies a search over the space of BN structures alternating between two steps: an optimization of the BN parameters (usually by means of the em algorithm) and a structural search for model selection. In this paper, we propose to perform the optimization of the BN parameters using an alternative approach to the em algorithm: the BC + em method. We provide experimental results to show that our proposal results in a more effective and efficient version of the Bayesian Structural em algorithm for learning BNs for clustering. (C) 2000 Elsevier Science B.V. All rights reserved.
Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based em algorithm for ...
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Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based em algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in em algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed em algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach.
New acceleration schemes and restarting procedures are defined and studied in view of application to the em algorithm. In most cases the introduced algorithms circumvent the problems of stagnation and degeneracy. Thei...
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New acceleration schemes and restarting procedures are defined and studied in view of application to the em algorithm. In most cases the introduced algorithms circumvent the problems of stagnation and degeneracy. Their behavior is analyzed on real data sets. (c) 2007 Elsevier B.V. All rights reserved.
This paper focuses on the problem of monitoring/estimating process parameters in the insufficient case when only imprecise and uncertain information can be obtained, possibly due to limited precision and reliability o...
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This paper focuses on the problem of monitoring/estimating process parameters in the insufficient case when only imprecise and uncertain information can be obtained, possibly due to limited precision and reliability of sensors in industries. To solve this problem, a constrained fuzzy evidential multivariate model is proposed as a soft sensor to monitor imprecise and uncertain process parameters. The most challenging task involved in the modeling is how to identify structure parameters of the monitor model, especially under sets of constraints. To tackle this challenge, we represent the imprecise and uncertain information as fuzzy belief functions in the evidence theory framework, and then propose a restricted fuzzy evidential Expectation-Conditional Maximization algorithm (RFE2CM) for maximum likelihood estimation from fuzzy belief functions under linear inequality constraints. Also, the convergence property of the restricted fuzzy evidential em algorithm is discussed. In order to validate the performance of the proposed model and algorithm, some numerical simulations are conducted as well as an experimental simulation on a real ball mill in a power plant. The numerical and experimental simulation results show that the proposed model and algorithm can not only be feasibly applicable to monitor the process parameters in insufficient informatics cases, but also have high prediction accuracy with small mean square errors.
The em algorithm is a sophisticated method for estimating statistical models with hidden variables based on the Kullback-Leibler divergence. A natural extension of the Kullback-Leibler divergence is given by a class o...
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The em algorithm is a sophisticated method for estimating statistical models with hidden variables based on the Kullback-Leibler divergence. A natural extension of the Kullback-Leibler divergence is given by a class of Bregman divergences, which in general enjoy robustness to contamination data in statistical inference. In this paper, a modification of the em algorithm based on the Bregman divergence is proposed for estimating finite mixture models. The proposed algorithm is geometrically interpreted as a sequence of projections induced from the Bregman divergence. Since a rigorous algorithm includes a nonlinear optimization procedure, two simplification methods for reducing computational difficulty are also discussed from a geometrical viewpoint. Numerical experiments on a toy problem are carried out to confirm appropriateness of the simplifications.
Clustering high dimensional data has become a challenge in data mining due to the curse of dimension-ality. To solve this problem, subspace clustering has been defined as an extension of traditional clustering that se...
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Clustering high dimensional data has become a challenge in data mining due to the curse of dimension-ality. To solve this problem, subspace clustering has been defined as an extension of traditional clustering that seeks to find clusters in subspaces spanned by different combinations of dimensions within a dataset. This paper presents a new subspace clustering algorithm that calcu-lates the local feature weights automatically in an em-based clustering process. In the algorithm, the features are locally weighted by using a new unsupervised weight-ing method, as a means to minimize a proposed cluster-ing criterion that takes into account both the average intra-clusters compactness and the average inter-clusters separation for subspace clustering. For the purposes of capturing accurate subspace information, an additional outlier detection process is presented to identify the pos-sible local outliers of subspace clusters, and is embedded between the E-step and M-step of the algorithm. The method has been evaluated in clustering real-world gene expression data and high dimensional artificial data with outliers, and the experimental results have shown its effectiveness.
This paper proposes an iterative maximum a posteriori probability (MAP) receiver for multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) mobile communications. For exploiting th...
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This paper proposes an iterative maximum a posteriori probability (MAP) receiver for multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) mobile communications. For exploiting the space, time, and frequency diversity, the low-density parity-check code (LDPC) is used as a channel coding with a built-in interleaver. The receiver employs the expectation maximization (em) algorithm so as to perform the MAP symbol detection with reasonable computational complexity. The minimum mean square error (MMSE), recursive least squares (RLS), and least mean square (LMS) algorithms are theoretically derived for the channel estimation within this framework. Furthermore, the proposed receiver performs a new scheme called backward symbol detection (BSD), in which the signal detection uses the channel impulse response that is estimated one OFDM symbol later. The advantage of BSD, which is explained from the viewpoint of the message passing algorithm, is that BSD can exploit information on the both precedent and subsequent OFDM symbols, similarly to RLS with smoothing and removing (SR-RLS) [25]. In comparison with SR-RLS, BSD reduces the complexity at the cost of packet error rate (PER) performance. Computer simulations show that the receiver employing RLS for the channel estimation outperforms the ones employing MMSE or LMS, and that BSD can improve the PER performance of the ones employing RLS or LMS.
The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in...
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The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.
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