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检索条件"主题词=Expectation Maximization Algorithm"

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Agnostic Learning of Mixed Linear Regressions with EM and AM algorithms 41

Agnostic Learning of Mixed Linear Regressions with EM and AM...

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41st International Conference on Machine Learning, ICML 2024

作者： Ghosh, Avishek Mazumdar, Arya Centre for Machine Intelligence for Data Sciences Indian Institute of Technology Bombay India Halıcıoğlu Data Science Institute University of California San Diego United States

Mixed linear regression is a well-studied problem in parametric statistics and machine learning. Given a set of samples, tuples of covariates and labels, the task of mixed linear regression is to find a small list of linear relationships that best fit the samples. Usually it is assumed that the label is generated stochastically by randomly selecting one of two or more linear functions, applying this chosen function to the covariates, and potentially introducing noise to the result. In that situation, the objective is to estimate the ground-truth linear functions up to some parameter error. The popular expectation maximization (EM) and alternating minimization (AM) algorithms have been previously analyzed for this. In this paper, we consider the more general problem of agnostic learning of mixed linear regression from samples, without such generative models. In particular, we show that the AM and EM algorithms, under standard conditions of separability and good initialization, lead to agnostic learning in mixed linear regression by converging to the population loss minimizers, for suitably defined loss functions. In some sense, this shows the strength of AM and EM algorithms that converges to "optimal solutions" even in the absence of realizable generative models. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Nonparametric Identification Modeling of Ship Manoeuvering Motion Based on Gaussian Mixture Model

Nonparametric Identification Modeling of Ship Manoeuvering M...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Jiao, Xuechao Ren, Junsheng Fan, Junhao Hu, Yu Dalian Maritime University Navigation College Dalian China

ISBN: (纸本)9798331504755

The dynamic simulation of the ship dynamics model using non-parametric identification can achieve high-precision simulation of the ship simulator and adaptive control of the intelligent *** order to reduce the error in the area with large changes and improve the recognition efficiency, this paper improves the traditional Gaussian mixture model (GMM) and proposes a non-parametric identification model based on the improved GMM to simulate and predict the motion state of the ship. The GMM is trained with multidimensional data such as hydrodynamics and rudder angle, the traditional initial model data is improved in sections, the expectation maximization (EM) algorithm is used for parameter optimization, and the improved model parameters are obtained by calculating the modified Gaussian mixture coefficients. The hydrodynamics of the ship are calculated by Gaussian mixture regression using the trained model and the predicted rudder angle, and the maneuvering motion state is solved by the Runge-Kutta method. The feasibility and effectiveness of this method are validated through experiments on the motion state of different rudder angles using turning test and zigzag test. © 2024 IEEE.

关键词： expectation maximization algorithm

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Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression 41

Unveiling the Cycloid Trajectory of EM Iterations in Mixed L...

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41st International Conference on Machine Learning, ICML 2024

作者： Luo, Zhankun Hashemi, Abolfazl School of Electrical and Computer Engineering Purdue University IN United States

We study the trajectory of iterations and the convergence rates of the expectation-maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goal of MLR is to learn the regression models from unlabeled observations. The EM algorithm finds extensive applications in solving the mixture of linear regressions. Recent results have established the super-linear convergence of EM for 2MLR in the noiseless and high SNR settings under some assumptions and its global convergence rate with random initialization has been affirmed. However, the exponent of convergence has not been theoretically estimated and the geometric properties of the trajectory of EM iterations are not well-understood. In this paper, first, using Bessel functions we provide explicit closed-form expressions for the EM updates under all SNR regimes. Then, in the noiseless setting, we completely characterize the behavior of EM iterations by deriving a recurrence relation at the population level and notably show that all the iterations lie on a certain cycloid. Based on this new trajectory-based analysis, we exhibit the theoretical estimate for the exponent of super-linear convergence and further improve the statistical error bound at the finite-sample level. Our analysis provides a new framework for studying the behavior of EM for Mixed Linear Regression. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Temporal Elections: Welfare, Strategyproofness, and Proportionality 27

Temporal Elections: Welfare, Strategyproofness, and Proporti...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Elkind, Edith Neoh, Tzeh Yuan Teh, Nicholas University of Oxford United Kingdom Alan Turing Insitute United Kingdom Agency for Science Technology and Research Singapore

ISBN: (纸本)9781643685489

We investigate a model of sequential decision-making where a single alternative is chosen at each *** focus on two objectives-utilitarian welfare (UTIL) and egalitarian welfare (EGAL)-and consider the computational complexity of the associated maximization problems, as well as their compatibility with strategyproofness and *** observe that maximizing UTIL is easy, but the corresponding decision problem for EGAL is NP-complete even in restricted *** complement this hardness result for EGAL with parameterized complexity analysis and an approximation ***, we show that, while a mechanism that outputs a UTIL outcome is strategyproof, all deterministic mechanisms for computing EGAL outcomes fail a very weak variant of strategyproofness, called non-obvious manipulability (NOM).However, we show that when agents have non-empty approval sets at each timestep, choosing an EGAL-maximizing outcome while breaking ties lexicographically satisfies *** proportionality, we prove that a proportional (PROP) outcome can be computed efficiently, but finding an outcome that maximizes UTIL while guaranteeing PROP is *** also derive upper and lower bounds on the price of proportionality with respect to UTIL and EGAL. © 2024 The Authors.

关键词： expectation maximization algorithm

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Leveraging Quantum Artificial Intelligence for Intelligent Face Recognition on the Internet of Medical Things (IoMT) for Smart City Surveillance 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Chengxi, Hong Rajawat, Anand Singh Goyal, S.B. Solanki, Ram Kumar Chengyi University College Jimei University Xiamen361021 China School of Computers Science & Engineering Sandip University Nashik422213 India Faculty of Information Technology City University Malaysia Petaling Jaya46100 Malaysia

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

Within the contemporary context of smart city infrastructures, the Internet of Medical Things (IoMT) has the capacity to instigate a revolution in urban monitoring. Despite recent advancements, conventional face recognition algorithms continue to encounter predicaments such as scalability, economy, and accuracy. In this scholarly article, we aim to explore the enhancement of face recognition techniques by incorporating Quantum Artificial Intelligence (AI), specifically QAEM, into the IoMT framework. Our research specifically focuses on the modus operandi of this integration. The amalgamation of edge devices for preliminary face identification and the utilization of quantum servers for advanced recognition facilitates this approach to deliver improved recognition accuracy, reduced calculation times, and heightened scalability, while also improving the ease of scalability. Our findings emphasize the potential of QAEM as both a standard for future quantum-enhanced applications in the realm of smart cities and as a tool for ameliorated surveillance in those same cities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： expectation maximization algorithm

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Closing the Data Gap: A Comparative Study of Missing Value Imputation algorithms in Time Series Datasets 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Dolatabadi, Sepideh Hassankhani Budinská, Ivana Behmaneshpour, Rafe Gatial, Emil Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia Rahbar Farayand Arya Company Department of Maintenance Bratislava Slovakia

ISBN: (数字)9783031535529

ISBN: (纸本)9783031535512

The presence of missing values in time series datasets poses significant challenges for accurate data analysis and modeling. In this paper, we present a comparative study of missing value imputation algorithms applied to time series datasets collected from various sensors over a period of six months. The goal of this study is to bridge the data gap by effectively replacing missing values and assessing the performance of three common imputation algorithms for time series: K-Nearest Neighbors (KNN) imputer, expectation-maximization (EM), and Multiple Imputation by Chained Equations (MICE). To evaluate the performance of the imputation techniques, we employed Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics. Through rigorous experimentation and analysis, we found that each algorithm exhibited varying degrees of effectiveness in handling missing values within the time series datasets. Our findings highlight the importance of choosing an appropriate imputation algorithm based on the characteristics of the dataset and the specific requirements of the analysis. The results also demonstrate the potential of the MICE imputer in closing the data gap and improving the accuracy of subsequent analyses on time series sensor data. Overall, this study provides valuable insights into the performance and suitability of different missing value imputation algorithms for time series datasets, facilitating better decision-making and enhancing the reliability of data-driven applications in various domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： expectation maximization algorithm

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Research on Language Recognition algorithms Based on Segment-Level Features and Automatic Identification 6

Research on Language Recognition Algorithms Based on Segment...

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6th International Conference on Artificial Intelligence and Computer Applications, ICAICA 2024

作者： Zu, Le Kang, Yong Gao, Jie He, Shanshan School of General Education Wannan Medical College Wuhu241002 China

ISBN: (纸本)9798350377255

Language recognition technology plays a crucial role in automated speech processing within multilingual environments, particularly under the globalized context where the increasing linguistic diversity poses higher demands on language recognition systems. Due to their robustness and efficiency, segment-level features and automatic identification algorithms have gradually become focal points of research. This paper explores a variety of language signal feature parameters, including RASTA-PLP parameters, MFCC parameters, RASTA-MFCC parameters, and CMS-MFCC parameters, as well as extraction methods for sliding difference cepstrum parameters and their applications in language recognition. Gaussian Mixture Models, along with their improved expectation-maximization algorithms and greedy expectation-maximization algorithms, are thoroughly analyzed to enhance the accuracy and robustness of language recognition. Preliminary experiments verify the performance of different feature parameters and algorithms on actual datasets, demonstrating that RASTA and CMS techniques significantly enhance the MFCC parameters. © 2024 IEEE.

关键词： expectation maximization algorithm

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Structure-coupled Variational Bayesian Method for Building Layout Reconstruction

Structure-coupled Variational Bayesian Method for Building L...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Yin, Zixiang Zeng, Xiaolu Yang, Xiaopeng Liao, Jiancheng Gong, Junbo Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314001 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798350375909

This paper presents a structure-coupled sparse Bayesian learning method for building layout reconstruction using through-the-wall radar. We characterize the azimuth continuity of the wall and two-dimensional extensibility of corner into a hierarchical probabilistic model. Moreover, we employ a variational expectation maximization algorithm to perform Bayesian inference, which provides an internal building structure map. Meanwhile, a generalized approximate message passing algorithm is integrated to speed up operations by avoiding matrix inversion. Results based on real data validate that the proposed method can extract the walls and corners accurately, thus enabling building layout reconstruction completely. © 2024 IEEE.

关键词： expectation maximization algorithm

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Remaining Useful Life Prediction of Railway Balise Based on Wiener Process Under Varying Temperature Conditions 27

Remaining Useful Life Prediction of Railway Balise Based on ...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Li, Zhengjiao Zhao, Zishuo Liu, Jiang Cai, Baigen Lu, Debiao School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331505929

The balise transmission system (BTS) plays a vital role in ensuring reliable ground communication of railway traffic. However, due to the natural loss of circuit operation, harsh working environment and high operating frequency, its circuit components gradually degrade and lead to system failure. Combined with the significant influence of varying temperature on the degradation of circuit system, this paper proposes a new method for predicting the remaining useful life (RUL) of railway balise. Firstly, a Wiener degradation model based on the Arrhenius temperature model is proposed, which can calculate the degradation path curve at different temperatures. Secondly, a model parameter estimation method combining expectation maximization (EM) algorithm and Kalman filter algorithm is proposed. Then, the probability density function (PDF) of the balise RUL under varying temperature conditions is derived. Finally, the effectiveness of the proposed method is verified by an example. The results show that the proposed prediction method has higher accuracy. © 2024 IEEE.

关键词： expectation maximization algorithm

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Adaptive Radar Detection in Compound-Gaussian Environments with Missing Data 2

Adaptive Radar Detection in Compound-Gaussian Environments w...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Hao, Chenchen Rong, Yao Tang, Mengjiao Li, Fan Wang, Xin Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming China Civil Aviation Flight University of China Key Laboratory of Flight Techniques and Flight Safety Guanghan China

ISBN: (纸本)9798331515669

This paper addresses adaptive radar detection in scenarios with incomplete observations due to measurement errors, sensor failures, or outliers, where the target is embedded in compound Gaussian clutter with unknown covariance matrix. The detection problem is formulated as a binary hypothesis test, with a set of selection matrices introduced to represent the observations at each snapshot. At the stage of detector design, a two-step design strategy is employed: first, the Generalized Likelihood Ratio Test (GLRT) is derived assuming known covariance structure;then, the actual covariance matrix in the GLRT is replaced with its maximum likelihood estimate, which is calculated based on the non-missing components of the secondary data using an expectation-maximization (EM) algorithm developed in this paper. Numerical examples demonstrate the effectiveness of these detectors in compound Gaussian clutter environments, showing superior performance compared to the existing detector designed for scenarios with Gaussian interference and missing data, as well as the classical linear imputation method. © 2024 IEEE.

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