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A Unifying Review of Linear Gaussian Models
, 1999
"... Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observa ..."
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Cited by 351 (18 self)
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Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate
Prediction of Context Information Using Kalman Filter Theory 1 Context information prediction using
"... filter theory R.E. Kalman presented in 1960 a novel approach [3] for an efficient solution of the discretedata linear filtering problem from a computational point of view. The set of recursive equations usually called the Kalman filter has been exploited in a large number of application fields from ..."
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filter theory R.E. Kalman presented in 1960 a novel approach [3] for an efficient solution of the discretedata linear filtering problem from a computational point of view. The set of recursive equations usually called the Kalman filter has been exploited in a large number of application fields
Prediction of Context Information Using Kalman Filter Theory,” http://www.cs.ucl.ac.uk/staff/m.musolesi/papers/kalman.pdf
 UCL Research
"... the Kalman filter theory R.E. Kalman presented in 1960 a novel approach [3] for an efficient solution of the discretedata linear filtering problem from a computational point of view. The set of recursive equations usually called the Kalman filter has been exploited in a large number of application ..."
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Cited by 3 (3 self)
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the Kalman filter theory R.E. Kalman presented in 1960 a novel approach [3] for an efficient solution of the discretedata linear filtering problem from a computational point of view. The set of recursive equations usually called the Kalman filter has been exploited in a large number of application
Kalman filtering with intermittent observations
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 2004
"... Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays ..."
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Cited by 295 (41 self)
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Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication
Speech Analysis
, 1998
"... Contents 1 Introduction 4 1.1 What is Speech Analysis? . . . . . . . . . . . . . . . . . . . . 4 1.1.1 So what is an acoustic vector? . . . . . . . . . . . . . . 4 1.2 Why Speech Analysis? . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The problems of speech analysis . . . . . . . . . . . . . . ..."
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Cited by 359 (0 self)
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2.2 Linear filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Finite Impulse Response filters . . . . . . . . . . . . . 8 2.2.2 Infinite Impulse Response filters . . . . . . . . . . . . . 11 2.3 The source filter model of speech . . . . . . . . . . . . . . . . 12 3 Filter bank
A Boosted Particle Filter: Multitarget Detection and Tracking
 In ECCV
, 2004
"... The problem of tracking a varying number of nonrigid objects has two major di#culties. First, the observation models and target distributions can be highly nonlinear and nonGaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambig ..."
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Cited by 308 (7 self)
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The problem of tracking a varying number of nonrigid objects has two major di#culties. First, the observation models and target distributions can be highly nonlinear and nonGaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap
On the Implementation of an InteriorPoint Filter LineSearch Algorithm for LargeScale Nonlinear Programming
, 2004
"... We present a primaldual interiorpoint algorithm with a filter linesearch method for nonlinear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration ph ..."
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Cited by 294 (6 self)
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We present a primaldual interiorpoint algorithm with a filter linesearch method for nonlinear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration
Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit
, 2006
"... Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NPhard in general. We show here that for systems with ‘typical’/‘random ’ Φ, a good approximation to the sparsest solution is obtained by applying a fixed number of standard operations from linear algebra. Our pr ..."
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Cited by 274 (22 self)
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Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NPhard in general. We show here that for systems with ‘typical’/‘random ’ Φ, a good approximation to the sparsest solution is obtained by applying a fixed number of standard operations from linear algebra. Our
Hierarchical Bayesian Inference in the Visual Cortex
, 2002
"... this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 coul ..."
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Cited by 300 (2 self)
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disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The datadriven Markov Chain Monte Carlo approach
Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting
, 1995
"... Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear leastsquares (pseudoinverse and eigen a ..."
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Cited by 278 (8 self)
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Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear leastsquares (pseudoinverse and eigen
Results 11  20
of
4,537