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22nd International Congress of the European-Federation-for-Medical-Informatics on Medical Informatics Europe (MIE)

作者： Plechawska, Malgorzata Polanska, Joanna Lublin Univ Technol Inst Comp Sci Nadbystrzycka 36B PL-20618 Lublin Poland Silesian Tech Univ Inst Informat Technol Gliwice Poland

ISBN: (纸本)9781607504566

This article presents the method of the processing of mass spectrometry data. Mass spectra are modelled with Gaussian Mixture Models. Every peak of the spectrum is represented by a single Gaussian. Its parameters describe the location, height and width of the corresponding peak of the spectrum. An authorial version of the expectation Maximisation algorithm was used to perform all calculations. Errors were estimated with a virtual mass spectrometer. The discussed tool was originally designed to generate a set of spectra within defined parameters.

关键词： mass spectrometry GMM expectation maximization algorithm

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An Improved Initialization Method using Firefly Movement and Light Intensity for Better Clustering Performance 2019

An Improved Initialization Method using Firefly Movement and...

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2nd International Conference on Software Engineering and Information Management (ICSIM) / 2nd International Conference on Big Data and Smart Computing (ICBDSC)

作者： Masangcap, Maria Lolita G. Sison, Ariel M. Medina, Ruji P. Technol Inst Philippines Quezon City Philippines Emilio Aguinaldo Coll Manila Philippines

ISBN: (纸本)9781450366427

K-Means and EM algorithms are the most well-known clustering algorithms because they are simple, easy to understand and implement. However, both algorithms are sensitive to initial seeds which are randomly selected leading to slow convergence and less reliable clustering results. In this paper, an improved initialization method adopted the concept of light intensity and firefly movement to search for better initial seeds. Numerical experiments were conducted to evaluate the performance of the Enhanced K-Means and EM using faculty performance evaluation ratings as the dataset. The experiments showed that the implementation of the improved initialization method before the clustering process resulted in a higher intra-cluster and lower inter-cluster similarity. Also, there are fifty-nine percent (59%) and sixty-three percent (63%) decrease in the runtime execution while there are forty-four percent (44%) and twenty-seven percent (27%) fewer number of iterations recorded in the implementation of the enhanced KMeans and EM algorithms respectively.

关键词： Clustering K-Means algorithm expectation maximization algorithm Light Intensity Firefly Movement

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Joint Iterative Channel Estimation and Data Detection for MIMO-CDMA Systems over Frequency-Selective Fading Channels

Joint Iterative Channel Estimation and Data Detection for MI...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Assra, Ayman Hamouda, Walaa Youssef, Amr Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781424456383

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over frequency-selective fading channels. Based on the expectation-maximization (EM) algorithm, the proposed iterative receiver achieves a performance close to the optimum maximum-likelihood (ML) receiver. In addition, the performance of the proposed receiver is optimized through weight coefficients using the minimum mean-square error (MMSE) criterion. Compared to the single-user bound, our results show that the proposed receiver can mitigate the multiple-access interference and attain the full system diversity. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

关键词： MIMO communication MIMO-CDMA system channel estimation code division multiple access data detection technique diversity reception expectation maximization algorithm expectation-maximisation algorithm fading channels frequency selective fading channel full

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Remote Sensing Image Change Detection Method Based on Contextual Information

Remote Sensing Image Change Detection Method Based on Contex...

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2nd Sino-Foreign-Interchange Workshop on Intelligence Science and Intelligent Data Engineering (IScIDE)

作者： Li, Weihua Niu, Penghui Jia, Chunyang Telecommun Engn Inst Xian 710077 Shanxi Peoples R China

ISBN: (纸本)9783642319181;9783642319198

A Remote Sensing image change detection method based on contexture information is proposed. The difference image is constructed by PCA and subtraction operation. Firstly, the Hidden Markov Random Field (HMRF) model is applied to characterize the contexture-dependent information, and the Energy function of system is defined. Secondly, the Greedy EM algorithm is used to overcome the disadvantage of the EM algorithm that assumed the number of the mixture components is a known priori, the performance of the overall parameter estimation process depends on the given good initial settings excessively, and the estimated parameter can be resulted from some local optimum points. The distribution model structure and parameters are learned accurately to finds the best fit of the given data. Finally the changed area is obtained by using Iterated Conditional Modes (ICM) to optimize the energy function. Experiments show that the proposed method has virtues of preserving structural change and filtering noises.

关键词： change detection hidden markov random field expectation maximization algorithm

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Projection-based Scanned Image Enhancement

Projection-based Scanned Image Enhancement

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24th Internationational Conference on Digital Printing Technologies/Digital Fabrication 2008

作者： Ahmed, Mohamed Nooman Lexmark Int Inc Software Res Lexington KY USA

ISBN: (纸本)9780892082797

In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modeling the distribution of a novel intensity projection technique using a mixture of K Gaussians. Model parameters are then estimated using the expectation maximization (EM) algorithm. Using the proposed algorithm, halftone areas were successfully differentiated from text regions

关键词： expectation maximization algorithm

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THE PERTURBED PROX-PRECONDITIONED SPIDER algorithm FOR EM-BASED LARGE SCALE LEARNING 21

THE PERTURBED PROX-PRECONDITIONED SPIDER ALGORITHM FOR EM-BA...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Fort, G. Moulines, E. Univ Toulouse IMT F-31062 Toulouse France CNRS F-31062 Toulouse France Ecole Polytech CMAP Route Saclay F-91128 Palaiseau France

ISBN: (纸本)9781728157672

Incremental expectation maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each iteration. Nevertheless, these algorithms all assume that the conditional expectations of the sufficient statistics are explicit. In this paper, we propose a novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER), which builds on the Stochastic Path Integral Differential EstimatoR EM (SPIDER-EM) algorithm. The 3P-SPIDER algorithm addresses many intractabilities of the E-step of EM;it also deals with non-smooth regularization and convex constraint set. Numerical experiments show that 3P-SPIDER outperforms other incremental EM methods and discuss the role of some design parameters.

关键词： Statistical Learning Large Scale Learning expectation maximization algorithm Finite-sum Optimization Accelerated Stochastic Approximation Control Variates

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Robust soft sensor development using multi-rate measurements

Robust soft sensor development using multi-rate measurements

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Wu, Ouyang Kodamana, Hariprasad Jan, Nabil Magbool Tan, Ruomu Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2R3 Canada

Two different types of measurements are often available for the key quality variables in process industries- (a) an accurate "slow-rate" laboratory measurements, and (b) a less accurate "fast-rate" online analyser measurements. Also, the analyser measurements are prone to fail due to hardware issues. Therefore, the main objective of this work is to present a novel approach for developing an accurate, fast-rate, inferential model of quality variables which is robust to outliers. For this purpose, we present a maximum likelihood based approach to integrate the multi-rate output data in the model building task, using expectation maximization algorithm. The efficacy of the proposed approach is demonstrated using a simulation example. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： soft sensor static model multi-rate expectation maximization algorithm t-distribution flat-topped t-distribution

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Robust soft sensor development using multi-rate measurements

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IFAC-PapersOnLine 2017年第1期50卷 10190-10195页

作者： Wu O. Kodamana H. Jan N.M. Tan R. Huang B. Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2R3 Canada

Two different types of measurements are often available for the key quality variables in process industries - (a) an accurate “slow-rate” laboratory measurements, and (b) a less accurate “fast-rate” online analyser measurements. Also, the analyser measurements are prone to fail due to hardware issues. Therefore, the main objective of this work is to present a novel approach for developing an accurate, fast-rate, inferential model of quality variables which is robust to outliers. For this purpose, we present a maximum likelihood based approach to integrate the multi-rate output data in the model building task, using expectation maximization algorithm. The efficacy of the proposed approach is demonstrated using a simulation example. © 2017

关键词： expectation maximization algorithm flat-topped t—distribution multi-rate soft sensor static model t—distribution

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Robust Object Detection and Tracking Using a Space-Temporal Mutual Feedback Scheme

Robust Object Detection and Tracking Using a Space-Temporal ...

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International Symposium on Knowledge Acquisition and Modeling

作者： Li, Xuchao Bian, Suxuan Jinggangshan Univ Coll Informat Sci & Media Jian 343009 Jiangxi Peoples R China Jinggangshan Univ Coll Nursing Jian Jiangxi Peoples R China

ISBN: (纸本)9781424435296

A statistical object detection and tracking mutual feedback scheme, combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectation maximization (EM) Kalman filter algorithm, is proposed in this paper. In space object detection stage, PCA provides compact and decorrelated feature space, the tracked object feature is statistically represented as GMM in RGB color space, objects are detected by maximum a posteriori (MAP) estimation. In temporal tracking stage, the tracked object is determined by the Bhattacharyya similarity measurement, the object position of consecutive frame is predicted by EM Kalman filter algorithm. The integration of object detection and tracking spatio-temporal mutual feedback scheme can decrease the accumulation error. We have applied the proposed method to object detection and tracking under the partial occlusion and the changes of moving speed with encouraging results.

关键词： object detection and tracking Kalman filter expectation maximization algorithm feedback

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State Entropy Optimization in Markov Decision Processes 20

State Entropy Optimization in Markov Decision Processes

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Ma, Shuai Xia, Li Zhao, Qianchuan Sun Yat Sen Univ Sch Business Guangzhou Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350358513;9798350358520

We examine the state entropy optimization in both discounted and average Markov decision processes (MDPs). We suggest a total entropy optimization in a discounted setting, and solve both the entropy rate optimization and the total discounted entropy optimization with iterative algorithms. An optimal solution to entropy maximization ensures that the system remains as unpredictable as possible. Previous works apply nonlinear programming methods to either the total entropy or entropy rate optimizations. We present both value iteration and policy iteration for synthesizing entropy optimizing policies in ergodic MDPs. For each state in each iteration, the action distribution is optimized with convex optimization in entropy maximization problems. We illustrate the validity of the proposed algorithms in a numerical experiment.

关键词： expectation maximization algorithm

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