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

共 493 条记录，以下是231-240 订阅

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An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An Empirical-Bayes Approach to Recovering Linearly Constrain...

引用

5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Vila, Jeremy Schniter, Philip Ohio State Univ Dept ECE Columbus OH 43210 USA

ISBN: (纸本)9781467331463;9781467331449

We consider the recovery of an (approximately) sparse signal from noisy linear measurements, in the case that the signal is apriori known to be non-negative and obeys certain linear equality constraints. For this, we propose a novel empirical-Bayes approach that combines the Generalized Approximate Message Passing (GAMP) algorithm with the expectation maximization (EM) algorithm. To enforce both sparsity and nonnegativity, we employ an i.i.d Bernoulli non-negative Gaussian mixture (NNGM) prior and perform approximate minimum mean-squared error (MMSE) recovery of the signal using sum-product GAMP. To learn the NNGM parameters, we use the EM algorithm with a suitable initialization. Meanwhile, the linear equality constraints are enforced by augmenting GAMP's linear observation model with noiseless pseudo-measurements. Numerical experiments demonstrate the state-of-the art mean-squared-error and runtime of our approach.(1)

关键词： Nonnegative expectation-maximisation algorithm Linear measurement Mean square error signals enforcement Runtime Equality RECOVERING

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Experiment-Based Scatter Correction for Cone-Beam Computed Tomography Using the Statistical Method

Experiment-Based Scatter Correction for Cone-Beam Computed T...

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35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Aootaphao, Sorapong Thongvigitmanee, Saowapak S. Rajruangrabin, Jatuwat Junhunee, Parinya Thajchayapong, Pairash Image Technology Lab Intelligent Informatics Research Unit National Electronics and Computer Technology Center Thailand Medical Devices Lab Biomedical Engineering Research Unit National Metal and Materials Technology Center Thailand National Science and Technology Development Agency Pathumthani Thailand

ISBN: (纸本)9781457702167

Scatter signals in cone-beam computed tomography (CBCT) cause a significant problem that degrades image quality of reconstructed images, such as inaccuracy of CT numbers and cupping artifacts. In this paper, we will present an experiment-based scatter correction method by preprocessing projection images using a statistical model combined with experimental kernels. The convolution kernels are estimated by using different thickness of PMMA plates attached to a beam stop lead sheet such that the scatter signal values can be measure in the shadow area of the projection images caused by the lead sheet. The scatter signal values of different thickness levels can be measured in the shadow area of projection images caused by the lead sheet. Then, the projection images are convolved with the kernels that are derived from the actual measurement of scatter signals in PMMA plates. Finally, the primary signals can be estimated using the maximum likelihood expectation maximization method. Experimental results by using the proposed method show that the quality of the reconstruction images is significantly improved. The CT numbers become more accurate and the cupping artifact is reduced.

关键词： computerised tomography expectation-maximisation algorithm image reconstruction medical image processing statistical analysis

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SRATS: Software Reliability Assessment Tool on Spreadsheet (Experience Report)

SRATS: Software Reliability Assessment Tool on Spreadsheet (...

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IEEE 24th International Symposium on Software Reliability Engineering Workshops (ISSREW)

作者： Okamura, Hiroyuki Dohi, Tadashi Hiroshima Univ Grad Sch Engn Dept Informat Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9781479923663

This paper presents a software reliability assessment tool which is built as a Microsoft Excel AddIn. The tool handles 11 types of software reliability growth models (SRGMs) and easily provides the maximum likelihood estimates (MLEs) from input data on spreadsheets. The tool consists of two components: the user interface written by Visual Basic for Application (VBA) and the dynamic link library (DLL) written by C language. In the DLL, the state-of-the-art parameter estimation algorithms for SRGMs are implemented, which are developed from the EM (expectation-maximization) algorithm. These algorithms enable us to derive the MLEs for any patterns of data.

关键词： C language Visual BASIC expectation-maximisation algorithm maximum likelihood estimation parameter estimation software reliability software tools spreadsheet programs user interfaces

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Online Quantum Mixture Regression for Trajectory Learning by Demonstration

Online Quantum Mixture Regression for Trajectory Learning by...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Korkinof, Dimitrios Demiris, Yiannis Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2BT England

ISBN: (纸本)9781467363587

In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.

关键词： expectation-maximisation algorithm iterative methods learning (artificial intelligence) numerical stability regression analysis robots stochastic processes

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Skills Transfer Across Dissimilar Robots by Learning Context-Dependent Rewards

Skills Transfer Across Dissimilar Robots by Learning Context...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Malekzadeh, Milad S. Bruno, Danilo Calinon, Sylvain Nanayakkara, Thrishantha Caldwell, Darwin G. IIT Dept Adv Robot Via Morego 30 I-16163 Genoa Italy Univ London King Coll Div Engn London WC1E 7HU England

ISBN: (纸本)9781467363587

Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problems in which the robot autonomously extracts an optimal reward function that defines the goal of the task. By relying on Gaussian mixture models, the proposed approach learns how the different candidate reward functions are combined, and in which contexts or phases of the task they are relevant for explaining the user's demonstrations. The extracted reward profile is then exploited to improve the skill with a self-refinement approach based on expectation-maximization, allowing the imitator to reach a skill level that goes beyond the demonstrations. The approach can be used to reproduce a skill in different ways or to transfer tasks across robots of different structures. The proposed approach is tested in simulation with a new type of continuum robot (STIFF-FLOP), using kinesthetic demonstrations from a Barrett WAM manipulator.

关键词： Gaussian processes expectation-maximisation algorithm learning (artificial intelligence) manipulators robot programming

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Unsupervised Motion Learning from a Moving Platform

Unsupervised Motion Learning from a Moving Platform

引用

IEEE Intelligent Vehicles Symposium

作者： Romero-Cano, Victor Nieto, Juan I. Agamennoni, Gabriel Univ Sydney Australian Ctr Field Robot Sydney NSW 2006 Australia

ISBN: (纸本)9781479907946;9781479907953

Learning motion patterns in dynamic environments is a key component of any context-aware robotic system, and probabilistic mixture models provide a sound framework for mining these patterns. This paper presents an approach for learning motion models from trajectories provided by the tracking system of a moving platform. We present a learning approach in which a Linear Dynamical System (LDS) is augmented with a discrete hidden variable that has a number of states equal to the number of behaviours in the environment. As a result, a mixture of linear dynamical systems (MLDSs) capable of explaining several motion behaviours is developed. The model is learned by means of the expectation Maximization (EM) algorithm.

关键词： data mining driver information systems expectation-maximisation algorithm unsupervised learning

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An Iterative Method of Sensor Calibration in Participatory Sensing Network

An Iterative Method of Sensor Calibration in Participatory S...

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IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)

作者： Xiang, Chaocan Yang, Panlong PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769551043

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, sensors on smart phones are prone to the unknown measurement errors, requiring automatical calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by EM (expectation Maximization) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. Our algorithm can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized.

关键词： calibration expectation-maximisation algorithm measurement errors monitoring parameter estimation smart phones wireless sensor networks

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DREAMS: DFM Rule EvAluation using Manufactured Silicon

DREAMS: DFM Rule EvAluation using Manufactured Silicon

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32nd IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

作者： Blanton, R. D. Wang, F. Xue, C. Nag, P. K. Xue, Y. Li, X. Carnegie Mellon Univ Adv Test Chip Lab ECE Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781479910717

DREAMS (DFM Rule EvAluation using Manufactured Silicon) is a comprehensive methodology for evaluating the yield-preserving capabilities of a set of DFM (design for manufacturability) rules using the results of logic diagnosis performed on failed ICs. DREAMS is an improvement over prior art in that the distribution of rule violations over the diagnosis candidates and the entire design are taken into account along with the nature of the failure (e. g., bridge versus open) to appropriately weight the rules. Silicon and simulation results demonstrate the efficacy of the DREAMS methodology. Specifically, virtual data is used to demonstrate that the DFM rule most responsible for failure can be reliably identified even in light of the ambiguity inherent to a non-ideal diagnostic resolution, and a corresponding rule-violation distribution that is counter-intuitive. We also show that the combination of physically-aware diagnosis and the nature of the violated DFM rule can be used together to improve rule evaluation even further. Application of DREAMS to the diagnostic results from an in-production chip provides valuable insight in how specific DFM rules improve yield (or not) for a given design manufactured in particular facility. Finally, we also demonstrate that a significant artifact of DREAMS is a dramatic improvement in diagnostic resolution. This means that in addition to identifying the most ineffective DFM rule(s), validation of that outcome via physical failure analysis of failed chips can be eased due to the corresponding improvement in diagnostic resolution.

关键词： design for manufacture elemental semiconductors expectation-maximisation algorithm failure analysis integrated circuit design integrated circuit reliability integrated circuit testing integrated circuit yield silicon

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Learning Multiple Non-Linear Sub-Spaces using K-RBMs

Learning Multiple Non-Linear Sub-Spaces using K-RBMs

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chandra, Siddhartha Kumar, Shailesh Jawahar, C. V. IIIT Hyderabad CVIT Hyderabad Andhra Pradesh India Google Hyderabad Andhra Pradesh India

ISBN: (纸本)9780769549897

Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, preserving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for natural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple non-linear subspaces. We propose a framework that uses K Restricted Boltzmann Machines (K-RBMS) to learn multiple non-linear subspaces in the raw image space. Projections of the image patches into these subspaces gives us features, which we use to build image representations. Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular image classification datasets show that representations based on our framework outperform the traditional feature representations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.

关键词： Boltzmann machines expectation-maximisation algorithm feature extraction image classification image representation learning (artificial intelligence) natural scenes

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Handwritten Line Detection via an EM algorithm

Handwritten Line Detection via an EM algorithm

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12th International Conference on Document Analysis and Recognition (ICDAR)

作者： Cruz, Francisco Ramos Terrades, Oriol Univ Autonoma Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549993

In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results.

关键词： document image processing expectation-maximisation algorithm handwriting recognition image segmentation

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