(Q34491608)

2 August 2017

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title

A Unifying Review of Linear Gaussian Models (English)

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1

Sam Roweis

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Zoubin Ghahramani

series ordinal

2

object named as

Zoubin Ghahramani

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language of work or name

English

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publication date

1 February 1999

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2 August 2017

number of pages

41

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published in

Neural Computation

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2 August 2017

volume

11

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2 August 2017

issue

2

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2 August 2017

page(s)

305-345

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A New Learning Algorithm for Blind Signal Separation

2 August 2017

cites work

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Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm

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Neural Networks and Principal Component Analysis: Learning from Examples Without Local Minima

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An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology

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Statistical Inference for Probabilistic Functions of Finite State Markov Chains

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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

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An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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GTM: The Generative Topographic Mapping

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Independent component analysis, A new concept?

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Maximum Likelihood from Incomplete Data via the EM Algorithm

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ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition

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A Rapidly Convergent Descent Method for Minimization

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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

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Supervised learning from incomplete data via an EM approach

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Regression with Input-dependent Noise: A Gaussian Process Treatment

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Modeling the Manifolds of Images of Handwritten Digits

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Generative models for discovering sparse distributed representations

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Recognizing Handwritten Digits Using Mixtures of Linear Models

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Autoencoders, Minimum Description Length and Helmholtz Free Energy

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Hierarchical Mixtures of Experts and the EM Algorithm

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Some contributions to maximum likelihood factor analysis

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A New Approach to Linear Filtering and Prediction Problems

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New Results in Linear Filtering and Prediction Theory

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Exponentiated Gradient versus Gradient Descent for Linear Predictors

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Self-Organized Formation of Topologically Correct Feature Maps

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Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems

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Least squares quantization in PCM

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Maximum Likelihood Competitive Learning

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Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA

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An introduction to hidden Markov models

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Solutions to the Linear Smoothing Problem

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Maximum likelihood estimates of linear dynamic systems

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EM Algorithms for PCA and SPCA

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EM algorithms for ML factor analysis

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Modeling Acoustic Correlations by Factor Analysis

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AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM

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Dynamic Linear Models With Switching

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Turbulence and the dynamics of coherent structures. I. Coherent structures

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Turbulence and the dynamics of coherent structures. II. Symmetries and transformations

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Turbulence and the dynamics of coherent structures. III. Dynamics and scaling

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Clustering Sequences with Hidden Markov Models

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Probabilistic Independence Networks for Hidden Markov Probability Models

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Mixtures of Probabilistic Principal Component Analyzers

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Training Neural Networks with Deficient Data

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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

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Identifiers

DOI

10.1162/089976699300016674

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2 August 2017

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DBLP Dataset 2021-01-02

28 January 2021

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