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K nearest neighbor reinforced expectation maximization method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 12585-12591页

作者： Aci, Mehmet Avci, Mutlu Cukurova Univ Dept Comp Engn Adana Turkey

K nearest neighbor and Bayesian methods are effective methods of machine learning. expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： K nearest neighbor method Bayesian method expectation maximization algorithm Hybrid method Classification Clustering

CLUSTERING IN HYPERBOLIC BALLS A PREPRINT

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arXiv 2025年

作者： Jaćimović, Vladimir Crnkić, Aladin Faculty of Natural Sciences and Mathematics University of Montenegro Cetinjski put bb. Podgorica81000 Montenegro Faculty of Technical Engineering University of Bihać Irfana Ljubijankića bb. Bihać77000 Bosnia and Herzegovina

The idea of representations of the data in negatively curved manifolds recently attracted a lot of attention and gave a rise to the new research direction named hyperbolic machine learning (ML). In order to unveil the full potential of this new paradigm, efficient techniques for data analysis and statistical modeling in hyperbolic spaces are necessary. In the present paper rigorous mathematical framework for clustering in hyperbolic spaces is established. First, we introduce the k-means clustering in hyperbolic balls, based on the novel definition of barycenter. Second, we present the expectation-maximization (EM) algorithm for learning mixtures of novel probability distributions in hyperbolic balls. In such a way we lay the foundation of unsupervised learning in hyperbolic spaces. © 2025, CC BY.

关键词： expectation maximization algorithm

Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams

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arXiv 2025年

作者： Morsomme, Raphaël Liang, C. Jason Mateja, Allyson Follmann, Dean A. O’Brien, Meagan P. Wang, Chenguang Fintzi, Jonathan Biostatistics Research Branch National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research FrederickMD United States Regeneron Pharmaceuticles TarrytownNY United States

We introduce a computationally efficient and general approach for utilizing multiple, possibly interval-censored, data streams to study complex biomedical endpoints using multistate semi-Markov models. Our motivating application is the REGEN-2069 trial, which investigated the protective efficacy (PE) of the monoclonal antibody combination REGEN-COV against SARS-CoV-2 when administered prophylactically to individuals in households at high risk of secondary transmission. Using data on symptom onset, episodic RT-qPCR sampling, and serological testing, we estimate the PE of REGEN-COV for asymptomatic infection, its effect on seroconversion following infection, and the duration of viral shedding. We find that REGEN-COV reduced the risk of asymptomatic infection and the duration of viral shedding, and led to lower rates of seroconversion among asymptomatically infected participants. Our algorithm for fitting semi-Markov models to interval-censored data employs a Monte Carlo expectation maximization (MCEM) algorithm combined with importance sampling to efficiently address the intractability of the marginal likelihood when data are intermittently observed. Our algorithm provide substantial computational improvements over existing methods and allows us to fit semi-parametric models despite complex coarsening of the data. © 2025, CC BY.

关键词： expectation maximization algorithm

REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence maximization

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arXiv 2025年

作者： Nguyen, Huyen Dam, Hieu Do, Nguyen Tran, Cong Pham, Cuong Posts and Telecommunications Institute of Technology Hanoi Viet Nam FPT University Swinburne Vietnam Hanoi campus Hanoi Viet Nam University of Florida Gainesville United States

In social online platforms, identifying influential seed users to maximize influence spread is a crucial as it can greatly diminish the cost and efforts required for information dissemination. While effective, traditional methods for Multiplex Influence maximization (MIM) have reached their performance limits, prompting the emergence of learning-based approaches. These novel methods aim for better generalization and scalability for more sizable graphs but face significant challenges, such as (1) inability to handle unknown diffusion patterns and (2) reliance on high-quality training samples. To address these issues, we propose the Reinforced Expert maximization framework (REM). REM leverages a Propagation Mixture of Experts technique to encode dynamic propagation of large multiplex networks effectively in order to generate enhanced influence propagation. Noticeably, REM treats a generative model as a policy to autonomously generate different seed sets and learn how to improve them from a Reinforcement Learning perspective. Extensive experiments on several real-world datasets demonstrate that REM surpasses state-of-the-art methods in terms of influence spread, scalability, and inference time in influence maximization tasks. © 2025, CC BY.

关键词： expectation maximization algorithm

Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160).

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arXiv 2025年

作者： Lefebvre, Alexandra Maréchal, Vincent Gloaguen, Arnaud Consortium, Obépine Lambert, Amaury Maday, Yvon UMR 7598 CNRS Paris75005 France Collège de France CNRS UMR 7241 INSERM U1050 PSL University Paris75005 France UMR S 938 Sorbonne Université Paris75012 France Centre National de Recherche en Génomique Humaine Institut de Biologie François Jacob CEA Université Paris-Saclay Évry91057 France École Normale Supérieure CNRS UMR 8197 INSERM U1024 PSL University Paris75005 France

Repeated waves of emerging variants during the SARS-CoV-2 pandemics have highlighted the urge of collecting longitudinal genomic data and developing statistical methods based on time series analyses for detecting new threatening lineages and estimating their fitness early in time. Most models study the evolution of the prevalence of particular lineages over time and require a prior classification of sequences into lineages. Such process is prone to induce delays and bias. More recently, few authors studied the evolution of the prevalence of mutations over time with alternative clustering approaches, avoiding specific lineage classification. Most of the aforementioned methods are however either non parametric or unsuited to pooled data characterizing, for instance, wastewater samples. The analysis of wastewater samples has recently been pointed out as a valuable complementary approach to clinical sample analysis, however the pooled nature of the data involves specific statistical challenges. In this context, we propose an alternative unsupervised method for clustering mutations according to their frequency trajectory over time and estimating group fitness from time series of pooled mutation prevalence data. Our model is a mixture of observed count data and latent group assignment and we use the expectation-maximization algorithm for model selection and parameter estimation. The application of our method to time series of SARS-CoV-2 sequencing data collected from wastewater treatment plants in France from October 2020 to April 2021 shows its ability to agnostically group mutations according to their probability of belonging to B.1.160, Alpha, Beta, B.1.177 variants with selection coefficient estimates per group in coherence with the viral dynamics in France reported by Nextstrain. Moreover, our method detected the Alpha variant as threatening as early as supervised methods (which track specific mutations over time) with the noticeable difference that, since unsupervis

关键词： expectation maximization algorithm

Competing-risk Weibull survival model with multiple causes

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arXiv 2025年

作者： Wang, Kai Mu, Yuqin Zhang, Shenyi Zhang, Zhengjun Ling, Chengxiu Academy of Pharmacy Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Mathematical Sciences University of Liverpool LiverpoolL693BX United Kingdom Department of Statistics School of Computer Data & Information Sciences University of Wisconsin MadisonWI53706 United States School of Economics and Management University of Chinese Academy of Sciences Beijing101418 China

The failure of a system can result from the simultaneous effects of multiple causes, where assigning a specific cause may be inappropriate or unavailable. Examples include contributing causes of death in epidemiology and the aetiology of neurodegenerative diseases like Alzheimer’s. We propose a parametric Weibull accelerated failure time model for multiple causes, incorporating a data-driven, individualized, and time-varying winning probability (relative importance) matrix. Using maximum likelihood estimation and the expectation-maximization (EM) algorithm, our approach enables simultaneous estimation of regression coefficients and relative cause importance, ensuring consistency and asymptotic normality. A simulation study and an application to Alzheimer’s disease demonstrate its effectiveness in addressing cause-mixture problems and identifying informative biomarker combinations, with comparisons to Weibull and Cox proportional hazards models. © 2025, CC BY-NC-ND.

关键词： expectation maximization algorithm

SE-BSFV: Online Subspace Learning based Shadow Enhancement and Background Suppression for ViSAR under Complex Background

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arXiv 2025年

作者： Yan, Shangqu Luo, Chenyang Fu, Yaowen Zhang, Wenpeng Yang, Wei Yu, Ruofeng The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Video synthetic aperture radar (ViSAR) has attracted substantial attention in the moving target detection (MTD) field due to its ability to continuously monitor changes in the target area. In ViSAR, the moving targets’ shadows will not offset and defocus, which is widely used as a feature for MTD. However, the shadows are difficult to distinguish from the low scattering region in the background, which will cause more missing and false alarms. Therefore, it is worth investigating how to enhance the distinction between the shadows and background. In this study, we proposed the Shadow Enhancement and Background Suppression for ViSAR (SE-BSFV) algorithm. The SE-BSFV algorithm is based on the low-rank representation (LRR) theory and adopts online subspace learning technique to enhance shadows and suppress background for ViSAR images. Firstly, we use a registration algorithm to register the ViSAR images and utilize Gaussian mixture distribution (GMD) to model the ViSAR data. Secondly, the knowledge learned from the previous frames is leveraged to estimate the GMD parameters of the current frame, and the expectation-maximization (EM) algorithm is used to estimate the subspace parameters. Then, the foreground matrix of the current frame can be obtained. Finally, the alternating direction method of multipliers (ADMM) is used to eliminate strong scattering objects in the foreground matrix to obtain the final results. The experimental results indicate that the SE-BSFV algorithm significantly enhances the shadows’ saliency and greatly improves the detection performance while ensuring efficiency compared with several other advanced pre-processing algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

FAST DATA INVERSION FOR HIGH-DIMENSIONAL DYNAMICAL SYSTEMS FROM NOISY MEASUREMENTS

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arXiv 2025年

作者： Lin, Yizi Liu, Xubo Segall, Paul Gu, Mengyang Department of Statistics and Applied Probability University of California Santa Barbara United States Department of Geophysics Stanford University United States

In this work, we develop a scalable approach for a flexible latent factor model for high-dimensional dynamical systems. Each latent factor process has its own correlation and variance parameters, and the orthogonal factor loading matrix can be either fixed or estimated. We utilize an orthogonal factor loading matrix that avoids computing the inversion of the posterior covariance matrix at each time of the Kalman filter, and derive closed-form expressions in an expectation-maximization algorithm for parameter estimation, which substantially reduces the computational complexity without approximation. Our study is motivated by inversely estimating slow slip events from geodetic data, such as continuous GPS measurements. Extensive simulated studies illustrate higher accuracy and scalability of our approach compared to alternatives. By applying our method to geodetic measurements in the Cascadia region, our estimated slip better agrees with independently measured seismic data of tremor events. The substantial acceleration from our method enables the use of massive noisy data for geological hazard quantification and other applications. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

A mirror descent approach to maximum likelihood estimation in latent variable models

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arXiv 2025年

作者： Crucinio, Francesca Romana Department of Economics Social Studies Applied Mathematics and Statistics University of Turin Italy

We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This approach is based on minimisation of a functional over the parameter space and the space of probability distributions and, contrary to other popular approaches, can be implemented when the latent variable takes values in discrete spaces. We provide a detailed theoretical analysis of both the mirror descent algorithm and its approximation via SMC. We experimentally show that the proposed algorithm outperforms standard expectation maximisation algorithms and is competitive with other popular methods for real-valued latent variables. © 2025, CC BY.

关键词： expectation maximization algorithm