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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kuropatwinski, Marcin Kleijn, Bastiaan VOICE LAB Gdynia Poland

ISBN: (纸本)9781479928934

In this paper, the estimation of speech AR parameters under noisy conditions is revisited. The em algorithm serving this purpose was first proposed by Gannot et al. We present an extensive experimental study along with a new approach to implement the E-step of the algorithm. The new realization of the E-step uses matrix computations instead of a Kalman filter. By appropriate rearrangement of the E-step, the complexity O(P(p + q)(3)) of the Kalman filter approach has been reduced to O(P log P), where P is the frame length, p is the speech order and q is the noise order. In practice, a speed up of the E-step of at least two orders of magnitude has been achieved. An extensive evaluation of the algorithm shows that em algorithm in its base form is unable to improve over a recent speech enhancement method proposed by Heusdens et al. and over an established Spectral Subtraction with Minimum Statistics method, as measured by various quality measures. However, with some modification it was possible to improve over these methods in terms of spectral distortion.

关键词： speech enhancement estimation em algorithm AR parameters

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Interference Suppression Using em algorithm in OFDM Transmissions

Interference Suppression Using EM Algorithm in OFDM Transmis...

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IEEE Global Communications Conference (GLOBECOM)

作者： Yoda, Naotoshi Ohtsuki, Tomoaki Mashino, Jun Sugiyama, Takatoshi Keio Univ Dept Informat & Comp Sci Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

ISBN: (纸本)9781479935123

Interference detection and suppression schemes are proposed for coded OFDM systems in the presence of narrow-band interference. Previously proposed interference detection schemes perform with a recursive forward error correcting (FEC) decoding, thus its calculation is heavy. The proposed scheme is based on an expectation maximization (em) algorithm to detect the interference before the FEC decoding process. The complexity is reduced because the FEC decoding is performed only once. Simulation results indicate that the proposed scheme achieves almost the same bit error rate performance as the conventional scheme, while decreasing the times of the FEC decoding.

关键词： OFDM modulation error statistics expectation-maximisation algorithm forward error correction interference suppression iterative decoding modulation coding radiocommunication recursive estimation signal detection em algorithm bit error rate performance coded OFDM transmission system expectation maximization algorithm interference suppression scheme narrowband interference detection scheme recursive FEC iterative decoding wireless communication Bit error rate Decoding Forward error correction Interference Iterative decoding OFDM Receivers

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Estimating a Cognitive Diagnostic Model for Multiple Strategies via the em algorithm

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APPLIED PSYCHOLOGICAL MEASURemENT 2014年第6期38卷 464-485页

作者： Huo, Yan de la Torre, Jimmy Rutgers State Univ New Brunswick NJ 08901 USA

The single-strategy deterministic, inputs, noisy "and'' gate (SS-DINA) model has previously been extended to a model called the multiple-strategy deterministic, inputs, noisy "and'' gate (MSDINA) model to address more complex situations where examinees can use multiple problem-solving strategies during the test. The main purpose of this article is to adapt an efficient estimation algorithm, the Expectation-Maximization algorithm, that can be used to fit the MS-DINA model when the joint attribute distribution is most general (i. e., saturated). The article also examines through a simulation study the impact of sample size and test length on the fit of the SS-DINA and MS-DINA models, and the implications of misfit on item parameter recovery and attribute classification accuracy. In addition, an analysis of fraction subtraction data is presented to illustrate the use of the algorithm with real data. Finally, the article concludes by discussing several important issues associated with multiple-strategies models for cognitive diagnosis.

关键词： cognitive diagnostic models multiple strategies DINA model em algorithm estimation

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Mixed Noise Image De-noising Based on em algorithm

Mixed Noise Image De-noising Based on EM Algorithm

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Shi Guicun Wang Feixing Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China

ISBN: (纸本)9783038351153

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model's parameter estimation is a key issue, because the accuracy of the noise model's parameters could affect the de-noising quality. In the case of mixed Gaussian noises, em algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

关键词： Gaussian Noise em algorithm Variance Estimation Wavelet Transform

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A parallel em algorithm for Gaussian Mixture Models implemented on a NUMA system using OpenMP

A parallel EM algorithm for Gaussian Mixture Models implemen...

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22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

作者： Kwedlo, Wojciech Bialystok Tech Univ Fac Comp Sci PL-15351 Bialystok Poland

ISBN: (纸本)9781479927289

In the paper the problem of estimation of Gaussian mixture model parameters is considered. A shared memory parallelization of the standard em algorithm, based on data decomposition, is proposed. Our approach uses a rowwise block striped decomposition of large arrays storing feature vectors and posterior probabilities. Additionally, some NUMA optimizations, which allow threads to use as much local memory as possible, exploiting the first-touch memory allocation policy of the Linux operating system, are described. The proposed method was implemented in OpenMP and tested on a 64-core system based on four AMD Opteron 6272 (codenamed "Interlagos") processors. The experimental results indicate, that on large datasets, the algorithm scales very well with respect to the number of cores, and NUMA optimizations significantly improve its performance.

关键词： Gaussian mixture model em algorithm OpenMP NUMA

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of Computer Science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(em)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition em algorithm label aggregation

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A Precise Hard-Cut em algorithm for Mixtures of Gaussian Processes

A Precise Hard-Cut EM Algorithm for Mixtures of Gaussian Pro...

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10th International Conference on Intelligent Computing (ICIC)

作者： Chen, Ziyi Ma, Jinwen Zhou, Yatong Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783319093390;9783319093383

The mixture of Gaussian processes (MGP) is a powerful framework for machine learning. However, its parameter learning or estimation is still a very challenging problem. In this paper, a precise hard-cut em algorithm is proposed for learning the parameters of the MGP without any approximation in the derivation. It is demonstrated by the experimental results that our proposed hard-cut em algorithm for MGP is feasible and even outperforms two available hard-cut em algorithms.

关键词： Mixture of Gaussian process Parameter learning em algorithm

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Reinforcement learning, particle filters and the em algorithm

Reinforcement learning, particle filters and the EM algorith...

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Information Theory and Applications Workshop (ITA)

作者： Borkar, Vivek S. Jain, Ankushkumar Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781479935901

We consider a parameter estimation problem for a Hidden Markov Model in the framework of particle filters. Using constructs from reinforcement learning for variance reduction in particle filters, a simulation based scheme is developed for estimating the partially observed log-likelihood function. A Kiefer-Wolfowitz like stochastic approximation scheme maximizes this function over the unknown parameter. The two procedures are performed on two different time scales, emulating the alternating 'expectation' and 'maximization' operations of the em algorithm. Numerical experiments are presented in support of the proposed scheme.

关键词： expectation-maximisation algorithm hidden Markov models learning (artificial intelligence) parameter estimation particle filtering (numerical methods) stochastic processes em algorithm Kiefer-Wolfowitz like stochastic approximation scheme expectation operations hidden Markov model maximization operations parameter estimation problem partially observed log-likelihood function particle filters reinforcement learning simulation based scheme Approximation methods Hidden Markov models Learning (artificial intelligence) Markov processes Monte Carlo methods Sections

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Modeling product degradation with heterogeneity: A general random-effects Wiener process approach

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IISE TRANSACTIONS 2024年

作者： Zhai, Qingqing Li, Yaqiu Chen, Piao Shanghai Univ Sch Management Shanghai Peoples R China China Elect Prod Reliabil & Environm Testing Res I Guangzhou Peoples R China Guangdong Prov Key Lab Elect Informat Prod Reliabi Guangzhou Peoples R China Zhejiang Univ ZJU UIUC Inst Haining Peoples R China

Degradation of many products in practical applications is often subject to unit-to-unit heterogeneity. Such heterogeneity can be attributed to the heterogeneous quality of the raw materials and the fluctuation of the manufacturing process, as well as the heterogeneous usage conditions and environments. The heterogeneity leads to the scattering of the degradation rates and diffusion intensities in the population. To model this phenomenon, this study proposes a general random-effects Wiener process model that accounts for the unit-to-unit heterogeneity in the degradation drift and the volatility simultaneously. In particular, the drift of the Wiener process is characterized by a normal distribution and the diffusion parameter is characterized by an independent inverse Gaussian distribution. The proposed model is flexible for characterization of heterogeneous degradation, and permits an analytically tractable model inference. An em algorithm incorporating the variational Bayesian method is developed to estimate the model parameters, and a parametric bootstrap approach is proposed to construct confidence intervals. The performance of the proposed model and the estimation algorithm is validated by Monte Carlo simulations. The degradation data of an infrared LED device and the wear data of the magnetic head of a hard disk drive are studied based on the proposed model. With comprehensive comparative studies, the good performance of the proposed model in fitting the real degradation data is validated.

关键词： Unit-to-unit heterogeneity random effects Wiener process variational Bayesian method em algorithm

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Liu-type shrinkage estimators for mixture of logistic regressions: an osteoporosis study

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年

作者： Ghanem, Elsayed Hatefi, Armin Usefi, Hamid Mem Univ Newfoundland Dept Math & Stat St John NF Canada

The logistic regression model is one of the most powerful statistical methods for the analysis of binary data. Logistic regression allows using a set of covariates to explain the binary responses. A mixture of logistic regression models is used to fit heterogeneous populations using an unsupervised learning approach. The multicollinearity problem is one of the most common problems in logistic and a mixture of logistic regressions, where the covariates are highly correlated. This problem results in unreliable maximum likelihood estimates for the regression coefficients. This research develops shrinkage methods to deal with the multicollinearity in a mixture of logistic regression models. These shrinkage methods include ridge and Liu-type estimators. Through extensive numerical studies, we show that the developed methods provide more reliable results in estimating the coefficients of the mixture. Finally, we applied shrinkage methods to analyze the status of bone disorders in women aged 50 and older.

关键词： Bone mineral data em algorithm Liu-type penalty Logistic regression Maximum likelihood Mixture models Multicollinearity Ridge penalty

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