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Improved Gaussian mixture modeling for accurate Wi-Fi based indoor localization systems

PHYSICAL COMMUNICATION 2020年 43卷 101218-101218页

作者： Alfakih, Marwan Keche, Mokhtar Benoudnine, Hadjira Meche, Abdelkrim Univ Sci & Technol Oran Mohamed Boudiaf Elect Dept Signals & Images Lab USTO MB BP 1505 El Mnaouer 31000 Oran Algeria Univ Abdelhamid Ibn Badis Fac Sci & Technol Dept Elect & Elect Engn BP 227 Route Belhacel Mostaganem Algeria

This paper deals with the problem of device's location using Wi-Fi signal strength in an indoor environment. In order to improve the location accuracy a new fingerprinting-probabilistic algorithm is proposed. In this algorithm, the probability distribution of the Received Signal Strength (RSS) by a Mobile User (MU) from several Access Points (AP) is approximated using the Gaussian Mixture Model (GMM) approach. This probability distribution is then exploited to improve the location estimation of the mobile user. To tackle the initialization problem of the expectation maximization (EM) algorithm, which is used to estimate the Gaussian mixture parameters, a deterministic initialization method is proposed. This method named Manual initialization (MI) initializes manually the mixture parameters directly from the data. To cope with the MI shortcomings, an Adaptive Initialization (AI) technique is proposed. The performance of the resulting method, named the Improved GMM (IGMM) is evaluated experimentally and compared to that of other robust methods. The obtained results illustrate the efficiency of the IGMM method. Compared to the standard initialization technique, the MI and AI initialization techniques improve the location accuracy by 14.4% and 18.4%, respectively. Compared to other location methods, the improvement brought about by the IGMM method, varies from 5.1% to 17.7%, when the MI technique is used, and from 10% to 21.5%, when the AI technique is used. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Indoor positioning Wi-Fi fingerprinting Received signal strength Gaussian mixture model expectation-maximization algorithm

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Domain problem-solving expert identification in community question answering

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EXPERT SYSTEMS 2020年第5期37卷 e12582-e12582页

作者： Tang, Weizhao Lu, Tun Gu, Hansu Zhang, Peng Gu, Ning Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shanghai Key Lab Data Sci Shanghai Peoples R China Shanghai Inst Intelligent Elect & Syst Shanghai Peoples R China Microsoft Inc Seattle WA 98052 USA

Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining well-designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian-Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.

关键词： expectation-maximization algorithm expert finding mixture model Q&A community

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An application niche for finite mixture models in forest resource surveys

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CANADIAN JOURNAL OF FOREST RESEARCH 2019年第11期49卷 1453-1462页

作者： Magnussen, Steen Naesset, Erik Gobakken, Terje Canadian Forest Serv Nat Resources Canada Pacific Forestry Ctr Victoria BC V8Z 1M5 Canada Norwegian Univ Life Sci Fac Environm Sci & Nat Resource Management POB 5003 NO-1432 As Norway

We propose design-based inference with finite mixture models (FMM) in settings where heterogeneity cannot be addressed by more conventional modelling. In FMM, a model is estimated for each of K latent model subgroups in a population under study. We evaluated the FMM approach with a difference estimator with K = 2 in 600 replications of simulated equal probability sampling from 12 artificial populations. An example with a forest population in southern Norway demonstrated a practical implementation. The artificial populations were composed of one, two, three, or four actual model subgroups generated from models that were either of the same form as the estimation model or different. We compare bias and variance in estimates of a population mean with standard results for K = 1. All estimates with K = 2 were nearly unbiased. Bias was largest when actual subgroups were clustered on y. Variances in sample means with K = 1 were 60% larger than with K = 2. An important reduction in variance with K = 2 was confirmed in the case study. A reliable estimate of variance requires a medium to large sample size.

关键词： bias simulated sampling subgroup models expectation-maximization algorithm model calibration

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Estimation, diagnostics, and extensions of nonparametric Hawkes processes with kernel functions

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JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2020年第1期3卷 391-412页

作者： Zhuang, Jiancang Inst Stat Math 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan Grad Univ Adv Studies SOKENDAI Dept Stat Sci 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan London Math Lab 8 Margravine Gardens London W6 8RH England

The Hawkes self-exciting model has become one of the most popular point-process models in many research areas in the natural and social sciences because of its capacity for investigating the clustering effect and positive interactions among individual events/particles. This article discusses a general nonparametric framework for the estimation, extensions, and post-estimation diagnostics of Hawkes models, in which we use the kernel functions as the basic smoothing tool.

关键词： Hawkes process Kernel estimate ETAS model Earthquake Crime expectation-maximization algorithm Stochastic reconstruction

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An integrated framework for reducing hospital readmissions using risk trajectories characterization and discharge timing optimization

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IISE Transactions on Healthcare Systems Engineering 2019年第2期9卷 172-185页

作者： Alaeddini, Adel Helm, Jonathan E. Shi, Pengyi Faruqui, Syed Hasib Akhter Department of Mechanical Engineering University of Texas at San Antonio San Antonio TX United States Department of Operations and Decision Technologies Kelley School of Business Indiana University Bloomington IN United States Krannert School of Management Purdue University West Lafayette IN United States

When patients leave the hospital for lower levels of care, they experience a risk of adverse events on a daily basis. The advent of value-based purchasing among other major initiatives has led to an increasing emphasis on reducing the occurrences of these post-discharge adverse events. This has spurred the development of new prediction technologies to identify which patients are at risk for an adverse event as well as actions to mitigate those risks. Those actions include pre-discharge and post-discharge interventions to reduce risk. However, traditional prediction models have been developed to support only post-discharge actions, predicting risk of adverse events at the time of discharge only. In this article, we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring. Our method combines a kernel approach for capturing the nonlinear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting estimation tractable. We then demonstrate how this prediction model could be used to support both types of interventions by developing a simple and easily implementable discharge timing optimization. © 2019, © 2019 “IISE”.

关键词： Cox Mixture Model discharge decision optimization expectation-maximization algorithm kernel PCA readmission predication

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Learning marked Markov modulated Poisson processes for online predictive analysis of attack scenarios 30

Learning marked Markov modulated Poisson processes for onlin...

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30th IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Carnevali, Laura Santoni, Francesco Vicario, Enrico Univ Florence Dept Informat Engn Via Santa Marta 3 I-50139 Florence Italy

ISBN: (纸本)9781728149820

Runtime predictive analysis of quantitative models can support software reliability in various application scenarios. The spread of logging technologies promotes approaches where such models are learned from observed events. We consider a system visiting transient states of a hidden process until reaching a final state and producing observations with stochastic arrival times and types conditioned by visited states, and we abstract it as a marked Markov modulated Poisson Process (MMMPP) with left-to right structure. We present an expectation-maximization (EM) algorithm that learns the MMMPP parameters from observation sequences acquired in repeated execution of the transient behavior, and we use the model at runtime to infer the current state of the process from actual observed events and to dynamically evaluate the remaining time to the final state. The approach is illustrated using synthetic datasets generated from a stochastic attack tree of the literature enriched with an observation model associating each state with an expected statistics of observation types and arrival times. Accuracy of prediction is evaluated under different variability of hidden states sojourn durations and of the observations arrival process, and compared against previous literature that mainly exploits either the timing or the types of observed events.

关键词： Marked Markov Modulated Poisson Processes expectation-maximization algorithm online failure prediction state-dependent event types and times attack trees

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An Online Approach to Estimate Parameters of Phase-Type Distributions 49

An Online Approach to Estimate Parameters of Phase-Type Dist...

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49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

作者： Buchholz, Peter Dohndorf, Iryna Kriege, Jan TU Dortmund Dept Comp Sci Dortmund Germany

ISBN: (纸本)9781728100562

The traditional expectation-maximization (EM) algorithm is a general purpose algorithm for maximum likelihood estimation in problems with incomplete data. Several variants of the algorithm exist to estimate the parameters of phase-type distributions (PHDs), a widely used class of distributions in performance and dependability modeling. EM algorithms are typical offline algorithms because they improve the likelihood function by iteratively running through a fixed sample. Nowadays data can be generated online in most systems such that offline algorithms seem to be outdated in this environment. This paper proposes an online EM algorithm for parameter estimation of PHDs. In contrast to the offline version, the online variant adds data immediately when it becomes available and includes no iteration. Different variants of the algorithms are proposed that exploit the specific structure of subclasses of PHDs like hyperexponential, hyper-Erlang or acyclic PHDs. The algorithm furthermore incorporates current methods to detect drifts or change points in a data stream and estimates a new PHD whenever such a behavior has been identified. Thus, the resulting distributions can be applied for online model prediction and for the generation of inhomogeneous PHDs as an extension of inhomogeneous Poisson processes. Numerical experiments with artificial and measured data streams show the applicability of the approach.

关键词： stochastic modeling phase-type distributions expectation-maximization algorithm online algorithm parameter estimation

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Semi-supervised logistic learning based on exponential tilt mixture models

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STAT 2020年第1期9卷

作者： Zhang, Xinwei Tan, Zhiqiang Rutgers State Univ Dept Stat Piscataway NJ 08854 USA

Consider semi-supervised learning for classification, where both labelled and unlabelled data are available for training. The goal is to exploit both datasets to achieve higher prediction accuracy than just using labelled data alone. We develop a semi-supervised logistic learning method based on exponential tilt mixture models by extending a statistical equivalence between logistic regression and exponential tilt modelling. We study maximum nonparametric likelihood estimation and derive novel objective functions that are shown to be Fisher probability consistent. We also propose regularized estimation and construct simple and highly interpretable expectation-maximization (EM) algorithms. Finally, we present numerical results that demonstrate the advantage of the proposed methods compared with existing methods.

关键词： empirical likelihood expectation-maximization algorithm exponential tilt model Fisher consistency logistic regression semi-supervised learning

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Identification of ARX Model with Multi-Gaussian Noises 19

Identification of ARX Model with Multi-Gaussian Noises

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19th International Conference on Control, Automation and Systems (ICCAS)

作者： Bai, Wentao Guo, Fan Chen, Lei Hao, Kuangrong Huang, Biao Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)9788993215182

In most industrial environments, the real process data points are usually subject to contamination from a variety of noises. Most of the traditional methods assume that the process noise is Gaussian white noise, which can lead to poor robustness of the model to abnormal data. In this paper, we consider an ARX model with multi-Gaussian white noises, assuming that the collected data is affected by different noise models and switching of noise model follows Markov chain probability. The parameters of the model are estimated by expectation-maximization (EM) algorithm. A numerical example and a continuous stirred tank reactor are employed to verify the effectiveness of the proposed algorithm.

关键词： ARX Markov chain expectation-maximization algorithm continuous stirred tank reactor

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Applying expectation-maximization Evaluation on Approximate Optimal Control 12

Applying Expectation-Maximization Evaluation on Approximate ...

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12th Annual IEEE International Systems Conference (SYSCON)

作者： Zhang, Songtao Dubay, Rickey Univ New Brunswick Dept Mech Engn Fredericton NB Canada

ISBN: (纸本)9781538636640

In this paper we proposed an approach of approximating optimal tracking via expectation-maximization (EM) evaluation. From the discussion of applying reinforcement learning (RL) for a system with unknown internal dynamics, we present the challenge of using a classical frame of Q-learning on a tracking task. Further we explained the idea of redefining the cost function (i.e. criterion) of Q-learning to satisfy the requirement for the system dynamic knowledge for the tracking task. We explained the advantages of dividing the original trajectory tracking task into two machine learning subtasks (i.e. learning the quadratic regulator and learning the baseline command generator) on-line. Details are given on the integration of the Q-learning frame and EM algorithm as well as the convergence to the optimum control via iterative estimation of an optimal regulator and a baseline generator. Initial simulation results of this approach using a second order system showed the ability of the Q-learning frame integrated with the EM algorithm approximates to the optimal tracking task.

关键词： Q-learning quadratic control expectation-maximization algorithm adaptive dynamic programming

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