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检索条件"主题词=Expectation-Maximisation algorithm"

共 495 条记录，以下是451-460 订阅

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A DIFFUSION-BASED DISTRIBUTED EM algorithm FOR DENSITY ESTIMATION IN WIRELESS SENSOR NETWORKS

A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIM...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Silvana Silva Pereira Alba Pages-Zamora Roberto Lopez-Valcarce SPCOM Group Universitat Politecnica de Catalunya-Barcelona Tech GPSC Universidade de Vigo

ISBN: (纸本)9781479903573

Distributed implementations of the expectation-Maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivate our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

关键词： Distributed algorithms expectation-maximization algorithms Gaussian distribution Unsupervised learning Wireless sensor networks expectation-maximisation algorithm Wireless sensor networks Unsupervised learning distributed algorithms Gaussian distribution Distribute Radio

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Inverse Reinforcement Learning using expectation Maximization in mixture models

Inverse Reinforcement Learning using Expectation Maximizatio...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J. Hahn A. M. Zoubir Signal Process. Group Tech. Univ. Darmstadt Darmstadt Germany

ISBN: (纸本)9781467369985

Reinforcement Learning (RL) is an attractive tool for learning optimal controllers in the sense of a given reward function. In conventional RL, usually an expert is required to design the reward function as the efficiency of RL strongly depends on the latter. An alternative has been presented by the concept of Inverse Reinforcement Learning (IRL), where the reward function is estimated from observed data. In this work, we propose a novel approach for IRL based on a generative probabilistic model of RL. We derive an expectation Maximization algorithm that is able to simultaneously estimate the reward and the optimal policy for finite state and action spaces, which can be easily extended for the infinite cases. By means of two toy examples, we show that the proposed algorithm works well even with a low number of observations and converges after only a few iterations.

关键词： expectation Maximization Inverse Reinforcement Learning Markov Decision Process Markov chain learning (artificial intelligence) Reward expectation-maximisation algorithm IRL Learning Probabilistic Model Inverse optimal strategy

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Convergence of the expectation-Maximization algorithm Through Discrete-Time Lyapunov Stability Theory

Convergence of the Expectation-Maximization Algorithm Throug...

引用

American Control Conference

作者： Orlando Romero Sarthak Chatterjee Sergio Pequito Rensselaer Polytechnic Institute Troy NY 12180 USA Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA

ISBN: (纸本)9781538679012;9781538679265

In this paper, we propose a dynamical systems perspective of the expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM algorithm as a nonlinear state-space dynamical system. The EM algorithm is widely adopted for data clustering and density estimation in statistics, control systems, and machine learning. This algorithm belongs to a large class of iterative algorithms known as proximal point methods. In particular, we re-interpret limit points of the EM algorithm and other local maximizers of the likelihood function it seeks to optimize as equilibria in its dynamical system representation. Furthermore, we propose to assess its convergence as asymptotic stability in the sense of Lyapunov. As a consequence, we proceed by leveraging recent results regarding discrete-time Lyapunov stability theory in order to establish asymptotic stability (and thus, convergence) in the dynamical system representation of the EM algorithm.

关键词： expectation-maximisation algorithm Converge DYNAMICAL SYSTEMS asymptotic stability region Lyapunov methods discrete-time data clustering Iterative algorithms

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High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity 11

High-dimensional regression with noisy and missing data: Pro...

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Annual Conference on Neural Information Processing Systems

作者： Po-Ling Loh Martin J. Wainwright Department of Statistics University of California Berkeley Berkeley CA 94720 Departments of Statistics and EECS University of California Berkeley Berkeley CA 94720

ISBN: (纸本)9781618395993

Although the standard formulations of prediction problems involve fully-observed and noiseless data drawn in an i.i.d. manner, many applications involve noisy and/or missing data, possibly involving dependencies. We study these issues in the context of high-dimensional sparse linear regression, and propose novel estimators for the cases of noisy, missing, and/or dependent data. Many standard approaches to noisy or missing data, such as those using the EM algorithm, lead to optimization problems that are inherently non-convex, and it is difficult to establish theoretical guarantees on practical algorithms. While our approach also involves optimizing non-convex programs, we are able to both analyze the statistical error associated with any global optimum, and prove that a simple projected gradient descent algorithm will converge in polynomial time to a small neighborhood of the set of global minimizers. On the statistical side, we provide non-asymptotic bounds that hold with high probability for the cases of noisy, missing, and/or dependent data. On the computational side, we prove that under the same types of conditions required for statistical consistency, the projected gradient descent algorithm will converge at geometric rates to a near-global minimizer. We illustrate these theoretical predictions with simulations, showing agreement with the predicted scalings.

关键词： missing data expectation-maximisation algorithm statistical error Non-Convex global optimum Polynomial time

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A Probabilistic Framework for Color-Based Point Set Registration

A Probabilistic Framework for Color-Based Point Set Registra...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Martin Danelljan Giulia Meneghetti Fahad Shahbaz Khan Michael Felsberg Computer Vision Laboratory Department of Electrical Engineering Linkoping University

ISBN: (纸本)9781467388528

In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

关键词： chromatic information Set of points expectation-maximisation algorithm failure rates REST ROOM Registration model parameters

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AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SENSING MULTITEMPORAL IMAGES

AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SE...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Daniel Capella Zanotta Hiran Zani Yosio Edemir Shimabukuro National Institute for Space Research - S?o José dos Campos - SP - Brazil

ISBN: (纸本)9781479911127

In this paper, a methodology for automatic detection of burned areas is suggested. The classification criterion is performed using Bayesian statistical parameter (mean and covariance matrix) extracted automatically using the expectation Maximization algorithm and taking into account the spectral similarity between burned and flooded areas. In this work the final process involves the application of morphological operators of erosion and dilation of images in order to insert information from the spatial context, refining the final map. Experiments were conducted to a TM-Landsat scene with areas affected by fires and seasonal flooding. The results show that the accuracy is increased with the consideration of flooding mask and the subsequent application of spatial context, reaching values up to 97% of accuracy when compared with a reference map.

关键词： Optical imagery Land surface change Pattern recognition Optical Imaging Pattern recognition Wetlands automatic detection expectation-maximisation algorithm AUTOMATIC REFERENCE MAPS images morphological operator Flooded areas remote sensing Burns spatial context variance covariance matrix

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EM algorithm based Channel Estimation for Amplify-and-Forward Relay Networks with Unknown Noise Correlation

EM Algorithm based Channel Estimation for Amplify-and-Forwar...

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IEEE Vehicular Technology Conference

作者： Chao Zhang Suhua Tang Pinyi Ren School of Electronic and Information Engineering XT an Jiaotong University Department of Smart Networks ATR Adaptive Communications Research Laboratories

ISBN: (纸本)9781467318808

Due to common interference or noise propagation, noise correlation between relays could occur in Amplify-and-Forward relay networks. To estimate channel coefficient, the noise covariance is required in traditional channel estimations. On the other hand, we also need channel coefficient to estimate the noise covariance. Therefore, traditional channel estimators can not be utilized when noise correlation is unknown. In this paper, we propose an expectation-Maximization algorithm based iterative channel estimator to solve this problem. Moreover, we analyze the modified Cramer-Rao bound to show the performance of the proposed channel estimation. Finally, simulation results show that the proposed channel estimator can work well in Amplify-and-Forward relay networks with unknown noise correlation.

关键词： Relay networks channel estimation expectation-maximisation algorithm Noise Channel Coefficient Unknown relay

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Automatic Discovery of Meaningful Object Parts with Latent CRFs

Automatic Discovery of Meaningful Object Parts with Latent C...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Paul Schnitzspan Stefan Roth Bernt Schiele Department of Computer Science

ISBN: (纸本)9781424469840;9781424469857

Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a balance between being flexible enough to model such variation and discriminative enough to detect objects in cluttered, real world scenes. Motivated by these challenges we propose a latent conditional random field (CRF) based on a flexible assembly of parts. By modeling part labels as hidden nodes and developing an EM algorithm for learning from class labels alone, this new approach enables the automatic discovery of semantically meaningful object part representations. To increase the flexibility and expressiveness of the model, we learn the pairwise structure of the underlying graphical model at the level of object part interactions. Efficient gradient-based techniques are used to estimate the structure of the domain of interest and carried forward to the multi-label or object part case. Our experiments illustrate the meaningfulness of the discovered parts and demonstrate state-of-the-art performance of the approach.

关键词： expectation-maximisation algorithm Flexible assembly Meaningfulness domain structure object recognition AUTOMATIC

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An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition 02

An Asynchronous Hidden Markov Model for Audio-Visual Speech ...

引用

Annual Conference on Neural Information Processing Systems

作者： Samy Bengio Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP) CP 592 rue du Simplon 4 1920 Martigny Switzerland

ISBN: (纸本)0262025507

This paper presents a novel Hidden Markov Model architecture to model the joint probability of pairs of asynchronous sequences describing the same event. It is based on two other Markovian models, namely Asynchronous Input/Output Hidden Markov Models and Pair Hidden Markov Models. An EM algorithm to train the model is presented, as well as a Viterbi decoder that can be used to obtain the optimal state sequence as well as the alignment between the two sequences. The model has been tested on an audio-visual speech recognition task using the M2VTS database and yielded robust performances under various noise conditions.

关键词： Speech recognition Hidden Markov models Viterbi decoders Asynchronous expectation-maximisation algorithm Pairing

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Collaborative PLSA for Multi-View Clustering

Collaborative PLSA for Multi-View Clustering

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International Conference on Pattern Recognition

作者： Yu Jiang Jing Liu Zechao Li Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467322164

Data has multi-view representations from various feature spaces in real world. Multi-view clustering algorithms allow leveraging information from multiple views of the data and this may substantially improve the clustering result obtained by using a single view. In this paper, we propose a novel algorithm called Collaborative PLSA (C-PLSA)for multi-view clustering, which works on the assumption that the clustering from one view should agree with the clustering from another view. The proposed C-PLSA combines individual PLSA models on two different views, and imports a regularizer to force the both clustering results agree across the two views. To solve the regularized problem, an alternative optimization algorithm based on generalized EM (GEM) is adopted for maximum likelihood estimation. Experiments on two real-world datasets, i.e., Reuters multilingual text and Corel images, demonstrate the improved performance of our proposed method over some related work.

关键词： expectation-maximisation algorithm optimisation pattern clustering

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