Results 1  10
of
87
Learning and Recognizing Human Dynamics in Video Sequences
, 1997
"... This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. Recognition in this framework is the succession of very general low level grouping mechanisms to increased specific and lea ..."
Abstract

Cited by 351 (2 self)
 Add to MetaCart
(Show Context)
This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. Recognition in this framework is the succession of very general low level grouping mechanisms to increased specific and learned model based grouping techniques at higher levels. Hard decision thresholds are delayed and resolved by higher level statistical models and temporal context. Lowlevel primitives are areas of coherent motion found by EM clustering, midlevel categories are simple movements represented by dynamical systems, and highlevel complex gestures are represented by Hidden Markov Models as successive phases of simple movements. We show how such a representation can be learned from training data, and apply it to the example of human gait recognition. 1 Introduction This paper addresses the problem of learning and recognizing human and other biological movements in video sequences of an unconstrai...
Accelerated Image Reconstruction using Ordered Subsets of Projection Data
 IEEE Trans. Med. Imag
, 1994
"... We define ordered subset processing for standard algorithms (such as Expectation Maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass thr ..."
Abstract

Cited by 293 (2 self)
 Add to MetaCart
(Show Context)
We define ordered subset processing for standard algorithms (such as Expectation Maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialise application of EM with that data subset. This approach is similar in concept to blockKaczmarz methods introduced by Eggermont et al [1] for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OSEM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OSEM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT the OSEM algorithm provides an orderofmagnitude acceleration ...
SpaceAlternating Generalized ExpectationMaximization Algorithm
 IEEE Trans. Signal Processing
, 1994
"... The expectationmaximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional loglikelihood of a single unobservable complete data space, rather than maximizing the intra ..."
Abstract

Cited by 194 (28 self)
 Add to MetaCart
(Show Context)
The expectationmaximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional loglikelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. This paper describes the spacealternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hiddendata spaces defined by the algorithm designer. We prove that the sequence of estimates monotonically increases the penalizedlikelihood objective, we derive asymptotic convergence rates, and we provide sufficient conditions for monotone convergence in norm. Two signal processing applicatio...
Globally Convergent Algorithms for Maximum a Posteriori Transmission Tomography
, 1995
"... This paper reviews and compares three maximum likelihood algorithms for transmission tomography. One of these algorithms is the EM algorithm, one is based on a convexity argument devised by De Pierro in the context of emission tomography, and one is an ad hoc gradient algorithm. The algorithms enjoy ..."
Abstract

Cited by 50 (18 self)
 Add to MetaCart
This paper reviews and compares three maximum likelihood algorithms for transmission tomography. One of these algorithms is the EM algorithm, one is based on a convexity argument devised by De Pierro in the context of emission tomography, and one is an ad hoc gradient algorithm. The algorithms enjoy desirable local and global convergence properties and combine gracefully with Bayesian smoothing priors. Preliminary numerical testing of the algorithms on simulated data suggest that the convex algorithm and the ad hoc gradient algorithm are computationally superior to the EM algorithm. This superiority stems from the larger number of exponentiations required by the EM algorithm. The convex and gradient algorithms are well adapted to parallel computing. Key words: maximum likelihood, smoothing prior, EM algorithm, convergence I. Introduction T HE value of the EM algorithm in emission tomography is now well established [22], [17], [24]. Not as widely appreciated is the potential of the EM...
Semiparametric Bayesian Analysis Of Survival Data
 Journal of the American Statistical Association
, 1996
"... this paper are motivated and aimed at analyzing some common types of survival data from different medical studies. We will center our attention to the following topics. ..."
Abstract

Cited by 44 (1 self)
 Add to MetaCart
this paper are motivated and aimed at analyzing some common types of survival data from different medical studies. We will center our attention to the following topics.
Accelerating EM for large databases
 Machine Learning
, 2001
"... The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires signi cant computational resources and has been dismissed as impractical for large databases. We presenttwo approaches that signi cantly reduce the ..."
Abstract

Cited by 44 (1 self)
 Add to MetaCart
The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires signi cant computational resources and has been dismissed as impractical for large databases. We presenttwo approaches that signi cantly reduce the computational cost of applying the EM algorithm to databases with a large number of cases, including databases with large dimensionality. Both approaches are based on partial Esteps for which we can use the results of Neal and Hinton (1998) to obtain the standard convergence guarantees of EM. The rst approach is a version of the incremental EM, described in Neal and Hinton (1998), which cycles through data cases in blocks. The number of cases in each block dramatically e ects the e ciency of the algorithm. We provide a method for selecting a near optimal block size. The second approach, which we call lazy EM, will, at scheduled iterations, evaluate the signi cance of each data case and then proceed for several iterations actively using only the signi cant cases. We demonstrate that both methods can signi cantly reduce computational costs through their application to highdimensional realworld and synthetic mixture modeling problems for large databases. Keywords: Expectation Maximization Algorithm, incremental EM, lazy EM, online EM, data blocking, mixture models, clustering.
On Stochastic Versions of the EM Algorithm
, 1995
"... We compare three different stochastic versions of the EM
algorithm: The SEM algorithm, the SAEM algorithm and the MCEM algorithm. We suggest that the most relevant contribution of the MCEM methodology is what we call the
simulated annealing MCEM algorithm, which turns out to be very close to SAEM. ..."
Abstract

Cited by 39 (1 self)
 Add to MetaCart
We compare three different stochastic versions of the EM
algorithm: The SEM algorithm, the SAEM algorithm and the MCEM algorithm. We suggest that the most relevant contribution of the MCEM methodology is what we call the
simulated annealing MCEM algorithm, which turns out to be very close to SAEM. We focus particularly on the mixture of
distributions problem. In this context, we review the available theoretical results on the convergence of these algorithms and on the behavior of SEM as the sample size tends to infinity. The second part is devoted to intensive Monte Carlo numerical simulations and a real data study. We show that, for some particular mixture situations, the SEM algorithm is almost always preferable to the EM and
simulated annealing versions SAEM and MCEM. For
some very intricate mixtures, however, none of these algorithms can be confidently used. Then, SEM can be used as an efficient data exploratory tool for locating significant maxima of the likelihood function. In the real data case, we show that the SEM stationary distribution provides a contrasted view of the loglikelihood by emphasizing sensible maxima.
Accelerated Quantification of Bayesian Networks with Incomplete Data
 In Proceedings of First International Conference on Knowledge Discovery and Data Mining
, 1995
"... Probabilistic expert systems based on Bayesian networks (BNs) require initial specification of both a qualitative graphical structure and quantitative assessment of conditional probability tables. This paper considers statistical batch learning of the probability tables on the basis of incomple ..."
Abstract

Cited by 33 (2 self)
 Add to MetaCart
Probabilistic expert systems based on Bayesian networks (BNs) require initial specification of both a qualitative graphical structure and quantitative assessment of conditional probability tables. This paper considers statistical batch learning of the probability tables on the basis of incomplete data and expert knowledge. The EM algorithm with a generalized conjugate gradient acceleration method has been dedicated to quantification of BNs by maximum posterior likelihood estimation for a superclass of the recursive graphical models. This new class of models allows a great variety of local functional restrictions to be imposed on the statistical model, which hereby extents the control and applicability of the constructed method for quantifying BNs. Introduction The construction of probabilistic expert systems (Pearl 1988, Andreassen et al. 1989) based on Bayesian networks (BNs) is often a challenging process. It is typically divided into two parts: First the constructi...
Averaging, Maximum Penalized Likelihood and Bayesian Estimation for Improving Gaussian Mixture Probability Density Estimates
 In
, 1996
"... We apply the idea of averaging ensembles of estimators to probability density estimation. ..."
Abstract

Cited by 33 (0 self)
 Add to MetaCart
We apply the idea of averaging ensembles of estimators to probability density estimation.
Kullback Proximal Algorithms for Maximum Likelihood Estimation
 IEEE Transactions on Information Theory
, 1998
"... Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization metho ..."
Abstract

Cited by 25 (6 self)
 Add to MetaCart
(Show Context)
Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods are based on a proximal point algorithm implemented with the KullbackLiebler (KL) divergence between posterior densities of the complete data as a proximal penalty function. When the proximal relaxation parameter is set to unity one obtains the classical expectation maximization (EM) algorithm. For a decreasing sequence of relaxation parameters, relaxed versions of EM are obtained which can have much faster asymptotic convergence without sacrice of monotonicity. We present an implementation of the algorithm using More's Trust Region update strategy. For illustration the method is applied to a...