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Robust soft sensor development using multi-rate measurements

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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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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Differential correlation for sequencing data

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BMC Research Notes 2017年第1期10卷 1-9页

作者： Siska, Charlotte Kechris, Katerina Computational Bioscience Program Department of Pharmacology School of Medicine University of Colorado Anschutz Medical Campus Aurora CO United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus Aurora CO United States

Background: Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the expectation maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs. Results: With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance. Conclusions: A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is

关键词： Posterior Probability Mixture Model expectation maximization algorithm Bullous Pemphigoid Median Absolute Deviation

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expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes

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STATISTICAL METHODS IN MEDICAL RESEARCH 2016年第4期25卷 1535-1563页

作者： Balakrishnan, Narayanaswamy Pal, Suvra McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada King Abdulaziz Univ Dept Stat Jeddah Saudi Arabia

Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell-Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence.

关键词： cure rate models long-term survivor Conway-Maxwell-Poisson distribution maximum likelihood estimators expectation maximization algorithm profile likelihood lifetime data Weibull distribution asymptotic variances Akaike's information criterion Bayesian information criterion

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First-Person Indoor Navigation via Vision-Inertial Data Fusion

First-Person Indoor Navigation via Vision-Inertial Data Fusi...

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IEEE/ION Position, Location and Navigation Symposium

作者： Amirreza Farnoosh Mohsen Nabian Pau Closas Sarah Ostadabbas Electrical and Computer Engineering Department Northeastern University Boston MA USA

In this paper, we aim to enhance the first-person indoor navigation and scene understanding experience by fusing inertial data collected from a smartphone carried by the user with the vision information obtained through the phone's camera. We employed the concept of vanishing directions together with the orthogonality constraints of the man-made environments in an expectation maximization framework to estimate person's orientation with respect to the known indoor coordinates from video frames. This framework allows to include prior information about camera rotation axis for better estimations as well as to select candidate edge-lines for estimation of hallways' depth and width from monocular video frames, and 3D modeling of the scene. Our proposed algorithm concurrently combines the vision-based estimated orientation with the inertial data using a Kalman filter in order to refine estimations and remove substantial measurement drift from inertial sensors. We evaluated the performance of our vision-inertial data fusion method on an IMU-augmented video recorded from a rotary hallway in which a participant completed a full lap. We demonstrated that this fusion provides virtually drift-free instantaneous information about the person's relative orientation. We were able to estimate hallways' depth and width, and generate a closed-path map from the rotary hallway over a roughly 60-meter lap.

关键词： Computer vision Data fusion expectation maximization algorithm In-door navigation Simultaneous localization and mapping (SLAM)

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Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第10期25卷 4677-4690页

作者： Cao, Xiangyong Zhao, Qian Meng, Deyu Chen, Yang Xu, Zongben Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China

Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L-1-norm and L-2-norm losses, which mainly deal with the Laplace and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as mixture of exponential power (MoEP) distributions and then proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture distribution is adapted from a series of preliminary super-or sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and then propose the PMoEP-MRF model. A generalized expectation maximization (GEM) algorithm and a variational GEM algorithm are designed to infer all parameters involved in the proposed PMoEP and the PMoEP-MRF model, respectively. The superiority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image denoising, and background subtraction.

关键词： Low-rank matrix factorization mixture of exponential power distributions expectation maximization algorithm face modeling hyperspectral image denoising background subtraction

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Classical and Bayesian Estimation for the Parameters of a Competing Risk Model Based on Minimum of Exponential and Gamma Failures

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IEEE TRANSACTIONS ON RELIABILITY 2016年第3期65卷 1522-1535页

作者： Ranjan, Rakesh Upadhyay, Satyanshu K. Banaras Hindu Univ Dept Stat Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ DST Ctr Interdisciplinary Math Sci Varanasi 221005 Uttar Pradesh India

The paper provides both classical and Bayesian estimation of the parameters of a competing risk model defined on the basis of minimum of exponential and gamma failure modes. Usually such situations are the examples of incomplete specification of data that naturally opens the way to expectation maximization algorithm for obtaining maximum likelihood estimates of model parameters. This incomplete specification of the data simultaneously explores the possibility of sampling importance resampling strategy with intermediate Markov chain Monte Carlo steps for the Bayesian estimation of parameters. Although this paper focuses primarily on estimation of model parameters, other inferential developments can be routinely done. Numerical illustration is provided based on both simulated and real-data examples.

关键词： Competing risk model exponential model expectation maximization algorithm gamma model increasing hazard rate sampling importance resampling

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Statistical Trilateration With Skew-t Distributed Errors in LTE Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第10期15卷 7114-7127页

作者： Muller, Philipp del Peral-Rosado, Jose A. Piche, Robert Seco-Granados, Gonzalo Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland Univ Autonoma Barcelona Dept Telecommun & Syst Engn Cerdanyola Del Valles 08193 Spain

Localization accuracy of trilateration methods in long term evolution (LTE) cellular networks, which are based on time-of-arrival, may be highly degraded due to multipath and non-line of sight conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an expectation maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4-MHz bandwidth. The EM method is compared with a standard nonlinear least squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.

关键词： Statistical trilateration skew-t distribution localization LTE expectation maximization algorithm

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Robust inference for mixed censored and binary response models with missing covariates

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STATISTICAL METHODS IN MEDICAL RESEARCH 2016年第5期25卷 1836-1853页

作者： Sarkar, Angshuman Das, Kalyan Sinha, Sanjoy K. Novartis Healthcare Pvt Ltd Hyderabad Andhra Pradesh India Univ Calcutta Dept Stat Kolkata W Bengal India Carleton Univ Sch Math & Stat Ottawa ON K1S 5B6 Canada

In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robust estimators. To avoid computational difficulties involving irreducibly high-dimensional integrals, we propose a Monte Carlo method based on the Metropolis algorithm for approximating the robust maximum likelihood estimators. We study the empirical properties of these estimators in simulations. We also illustrate the proposed robust method using clustered data on blood sugar content from a clinical trial of individuals who were investigated for diabetes.

关键词： binary model censored regression model expectation maximization algorithm metropolis algorithm missing data robust estimation

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Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2016年第1期18卷 1-19页

作者： Zhang, Huaiye Kim, Inyoung Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA

A finite mixture model has been used to fit the data from heterogeneous populations to many applications. An expectation maximization (EM) algorithm is the most popular method to estimate parameters in a finite mixture model. A Bayesian approach is another method for fitting a mixture model. However, the EM algorithm often converges to the local maximum regions, and it is sensitive to the choice of starting points. In the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sometimes converges to the local mode and is difficult to move to another mode. Hence, in this paper we propose a new method to improve the limitation of EM algorithm so that the EM can estimate the parameters at the global maximum region and to develop a more effective Bayesian approach so that the MCMC chain moves from one mode to another more easily in the mixture model. Our approach is developed by using both simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS). Although SA is a well-known approach for detecting distinct modes, the limitation of SA is the difficulty in choosing sequences of proper proposal distributions for a target distribution. Since ARMS uses a piecewise linear envelope function for a proposal distribution, we incorporate ARMS into an SA approach so that we can start a more proper proposal distribution and detect separate modes. As a result, we can detect the maximum region and estimate parameters for this global region. We refer to this approach as ARMS annealing. By putting together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM-ARMS annealing algorithm and a Bayesian-ARMS annealing approach. We compare our two approaches with traditional EM algorithm alone and Bayesian approach alone using simulation, showing that our two approaches are comparable to each other but perform better than EM algorithm alone and Bayesian approach alone. Our two approaches detect the global maxi

关键词： Adaptive rejection metropolis sampling expectation maximization algorithm Finite mixture models Simulated annealing

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