检索结果-内蒙古大学图书馆

Robust inference with incompleteness for logistic regression model

学校读者我要写书评

暂无评论

Signal Processing 2025年 236卷

作者： Cherifi, M. El Korso, M.N. Fortunati, S. Mesloub, A. Ferro-Famil, L. Lab. Traitement du signal Ecole Militaire Polytechnique BP 17 Bordj El Bahri Algeria University of Paris Saclay CNRS CentraleSupelec Laboratoire des signaux et systèmes Gif-sur-Yvette91190 France SAMOVAR Télécom SudParis Institut Polytechnique de Paris 19 Place Marguerite Perey Palaiseau91120 France ISAE-SUPAERO & CESBIO Toulouse France

Logistic regression models traditionally assume observed covariates. However, practical scenarios often involve missing data and outliers, which pose significant challenges. This short communication presents a new approach to solve these issues by integrating random covariates following a Student t-distribution within the framework of logistic regression. We propose a Robust Stochastic Approximation expectation–maximization algorithm suitable for Logistic Regression (REM-LR) that, in addition, is able to improve the resilience of the model against both missing values and outliers. © 2025 Elsevier B.V.

关键词： expectation maximization algorithm

Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking with a Low-cost Camera

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Ni, Peiyuan Chew, Chee Meng Ang, Marcelo H. Chirikjian, Gregory S. Department of Mechanical Engineering National University of Singapore Singapore Department of Mechanical Engineering University of Delaware NewarkDE19716 United States

Bin-picking of metal objects based on low-cost RGBD cameras may suffer errors due to sparse depth information and reflective part texture, leading to a need for manual labeling. To reduce the need for human intervention, we propose a framework consisting of a metric learning stage and a self-training stage. Specifically, to automate the handling of the data captured by a low-cost camera (LC), a multi-object pose reasoning (MoPR) algorithm is proposed which optimizes the data with depth, collision and boundary constraints. We adopt a symmetry-aware Lie-group based Bayesian Gaussian Mixture Model (SaL-BGMM) to filter the candidates combined with an expectation–maximization algorithm. Moreover, we also propose a weighted ranking information noise contrastive estimation (WR-InfoNCE) algorithm to ensure that the LC can learn a perceptual metric from reconstructed data for self-training of untrained or even unseen objects. Experiments show that our algorithm outperforms related popular works both on ROBI and our proposed Self-ROBI dataset. © 2025, CC BY.

关键词： expectation maximization algorithm

Seal Your Backdoor with Variational Defense

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sabolić, Ivan Grcić, Matej Šegvić, Siniša University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia

We propose VIBE, a model-agnostic framework that trains classifiers resilient to backdoor attacks. The key concept behind our approach is to treat malicious inputs and corrupted labels from the training dataset as observed random variables, while the actual clean labels are latent. VIBE then recovers the corresponding latent clean label posterior through variational inference. The resulting training procedure follows the expectation-maximization (EM) algorithm. The E-step infers the clean pseudolabels by solving an entropy-regularized optimal transport problem, while the M-step updates the classifier parameters via gradient descent. Being modular, VIBE can seamlessly integrate with recent advancements in self-supervised representation learning, which enhance its ability to resist backdoor attacks. We experimentally validate the method effectiveness against contemporary backdoor attacks on standard datasets, a large-scale setup with 1k classes, and a dataset poisoned with multiple attacks. VIBE consistently outperforms previous defenses across all tested scenarios. © 2025, CC BY-NC-SA.

关键词： expectation maximization algorithm

Learning to quantify graph nodes

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Micheli, Alessio Moreo, Alejandro Podda, Marco Sebastiani, Fabrizio Simoni, William Tortorella, Domenico Dipartimento di Informatica Università di Pisa Largo Bruno Pontecorvo 3 Pisa56127 Italy Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Via Giuseppe Moruzzi 1 Pisa56124 Italy

Network Quantification is the problem of estimating the class proportions in unlabeled subsets of graph nodes. When prior probability shift is at play, this task cannot be effectively addressed by first classifying the nodes and then counting the class predictions. In addition, unlike non-relational quantification on i.i.d. datapoints, Network Quantification demands enhanced flexibility to capture a broad range of connectivity patterns, resilience to the challenge of heterophily, and efficiency to scale to larger networks. To meet these stringent requirements we introduce XNQ, a novel method that synergizes the flexibility and efficiency of the unsupervised node embeddings computed by randomized recursive Graph Neural Networks, with an expectation-maximization algorithm that provides a robust quantification-aware adjustment to the output probabilities of a calibrated node classifier. We validate the design choices underpinning our method through comprehensive ablation experiments. In an extensive evaluation, we find that our approach consistently and significantly improves on the best Network Quantification methods to date, thereby setting the new state of the art for this challenging task. Simultaneously, it provides a training speed-up of up to 10×–100× over other graph learning based methods. © 2025, CC BY.

关键词： expectation maximization algorithm

Learning a Class of Mixed Linear Regressions: Global Convergence under General Data Conditions

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Liu, Yujing Liu, Zhixin Guo, Lei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100192 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

Mixed linear regression (MLR) has attracted increasing attention because of its great theoretical and practical importance in capturing nonlinear relationships by utilizing a mixture of linear regression sub-models. Although considerable efforts have been devoted to the learning problem of such systems, i.e., estimating data labels and identifying model parameters, most existing investigations employ the offline algorithm, impose the strict independent and identically distributed (i.i.d.) or persistent excitation (PE) conditions on the regressor data, and provide local convergence results only. In this paper, we investigate the recursive estimation and data clustering problems for a class of stochastic MLRs with two components. To address this inherently nonconvex optimization problem, we propose a novel two-step recursive identification algorithm to estimate the true parameters, where the direction vector and the scaling coefficient of the unknown parameters are estimated by the least squares and the expectation-maximization (EM) principles, respectively. Under a general data condition, which is much weaker than the traditional i.i.d. and PE conditions, we establish the global convergence and the convergence rate of the proposed identification algorithm for the first time. Furthermore, we prove that, without any excitation condition on the regressor data, the data clustering performance including the cumulative mis-classification error and the within-cluster error can be optimal asymptotically. Finally, we provide a numerical example to illustrate the performance of the proposed learning algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Regularized Parameter Estimation in Mixed Model Trace Regression

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Hultman, Ian Srivastava, Sanvesh Department of Statistics and Actuarial Science University of Iowa United States

We introduce mixed model trace regression (MMTR), a mixed model linear regression extension for scalar responses and high-dimensional matrix-valued covariates. MMTR’s fixed effects component is equivalent to trace regression, with an element-wise lasso penalty imposed on the regression coefficients matrix to facilitate the estimation of a sparse mean parameter. MMTR’s key innovation lies in modeling the covariance structure of matrix-variate random effects as a Kronecker product of low-rank row and column covariance matrices, enabling sparse estimation of the covariance parameter through low-rank constraints. We establish identifiability conditions for the estimation of row and column covariance matrices and use them for rank selection by applying group lasso regularization on the columns of their respective Cholesky factors. We develop an expectation-maximization (EM) algorithm extension for numerically stable parameter estimation in high-dimensional applications. MMTR achieves estimation accuracy comparable to leading regularized quasi-likelihood competitors across diverse simulation studies and attains the lowest mean square prediction error compared to its competitors on a publicly available image dataset. © 2025, CC BY.

关键词： expectation maximization algorithm

A Bayesian network approach to traffic flow forecasting

学校读者我要写书评

暂无评论

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2006年第1期7卷 124-132页

作者： Sun, SL Zhang, CS Yu, GQ Tsinghua Univ Dept Automat State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China NuTech Co Ltd Beijing 100084 Peoples R China

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.

关键词： Bayesian network expectation maximization algorithm Gaussian mixture model traffic flow forecasting

Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo expectation maximization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Yuheng Ye, Juan Li, Weiye Borchers, David L. Centre for Research into Ecological and Environmental Modelling School of Mathematics and Statistics University of St Andrews Fife St AndrewsKY16 9LZ United Kingdom School of Computer Science University of St Andrews North Haugh Fife St AndrewsKY16 9SX United Kingdom

Acoustic spatial capture-recapture (ASCR) surveys with an array of synchronized acoustic detectors can be an effective way of estimating animal density or call density. However, constructing the capture histories required for ASCR analysis is challenging, as recognizing which detections at different detectors are of which calls is not a trivial task. Because calls from different distances take different times to arrive at detectors, the order in which calls are detected is not necessarily the same as the order in which they are made, and without knowing which detections are of the same call, we do not know how many different calls are detected. We propose a Monte Carlo expectation-maximization (MCEM) estimation method to resolve this unknown call identity problem. To implement the MCEM method in this context, we sample the latent variables from a complete-data likelihood model in the expectation step and use a semi-complete-data likelihood or conditional likelihood in the maximization step. We use a parametric bootstrap to obtain confidence intervals. When we apply our method to a survey of moss frogs, it gives an estimate within 15% of the estimate obtained using data with call capture histories constructed by experts, and unlike this latter estimate, our confidence interval incorporates the uncertainty about call identities. Simulations show it to have a low bias (6%) and coverage probabilities close to the nominal 95% value. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Inference for a Failure Counting Process Partially Observed

学校读者我要写书评

暂无评论

IEEE TRANSACTIONS ON RELIABILITY 2015年第1期64卷 311-319页

作者： Guerineau, Lise Gouno, Evans Elect France Res & Dev Dept Reliabil Availabil Elect Networks Paris France Univ Bretagne Sud Lab Math Bretagne Atlantique Vannes France

We consider a system defined as a collection of two types of components. The number of failures of each component is described as a stochastic process, with one of the processes depending on the other. None of the processes is observed directly. The only available information is the number of type 1 components at risk in the system. Because of this missing data situation, different algorithms relying on an expectation maximization (EM) strategy are proposed to obtain the MLE of the intensity parameters for both processes so we can assess the reliability of type 1 and type 2 components. To overcome the computational limits of EM, a Monte Carlo EM (MCEM) algorithm using a Metropolis procedure is presented. Stochastic EM (SEM) algorithms including a Bayesian approach are also described. The methods are applied to simulated data to demonstrate their efficiency.

关键词： Bayesian estimation birth process expectation maximization algorithm maximum likelihood Metropolis algorithm Monte Carlo methods Poisson process