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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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Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

Adaptive Soft Sensor Modeling Based on Weighted Supervised L...

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

作者： Yao, Le Ge, Zhiqiang Yuan, Xiaofeng Wang, Peiliang Zhejiang Univ Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Huzhou Univ Sch Engn Huzhou 313000 Peoples R China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Hayes' rule for quality estimation However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Supervised latent factor analysis Weighted model expectation maximization algorithm Adaptive soft sensor Moving window Model integration Quality prediction

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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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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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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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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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A Study of Cluster Validity Indices for Real-Life Data 1

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16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Starczewski, Artur Krzyzak, Adam Czestochowa Tech Univ Inst Computat Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (数字)9783319590608

ISBN: (纸本)9783319590608;9783319590592

In this paper a study of several cluster validity indices for real-life data sets is presented. Moreover, a new version of validity index is also proposed. All these indices can be considered as a measure of data partitioning accuracy and the performance of them is demonstrated for real-life data sets, where three popular algorithms have been applied as underlying clustering techniques, namely the Complete-linkage, expectation maximization and K-means algorithms. The indices have been compared taking into account the number of clusters in a data set. The results are useful to choose the best validity index for a given data set.

关键词： Cluster validity index Complete-linkage clustering K-means clustering expectation maximization algorithm

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