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Classification of PolSAR data with the Complex Riesz distribution

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2025年

作者： Kammoun, Rayhan Kessentini, Sameh Zine, Raoudha Univ Sfax Fac Sci Sfax Lab Probabil & Stat BP 1171 Sfax 3000 Tunisia

This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and expectation-maximization (EM) algorithms to the Complex Riesz distribution. Furthermore, we derive the analytic expression of five stochastic distances (Kullback-Leibler, Bhattacharyya, R & eacute;nyi, Hellinger, and Chi-square) between Complex Riesz distributions. We assess the accuracy of the Complex Riesz EM algorithm on synthetic data generated by an extension of the Bartlett decomposition. Then, comparing the Complex Wishart and the Complex Riesz distributions on PolSAR data reveals that the latter performs better than the former. Finally, the EM algorithm for the Complex Riesz distribution serves to classify the actual data, and the discrimination potential of the five stochastic distances is discussed. These results in both experimental, ML, and EM algorithms suggest that most stochastic distances are significant, and mainly the 0. 7-order R & eacute;nyi.

关键词： Complex Riesz distribution expectation-maximization algorithm Hermitian random matrix maximum likelihood algorithm PolSAR classification stochastic distances

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DS-Net: A model driven network framework for lesion segmentation on fundus image

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KNOWLEDGE-BASED SYSTEMS 2025年 315卷

作者： Tan, Feiyu Wang, Yuhan Xie, Qi Fu, Jiahong Wang, Renzhen Meng, Deyu Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Shaanxi Peoples R China Zhejiang Lab Res Inst Interdisciplinary Innovat Res Ctr Intelligent Robot Hangzhou 311100 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Taipa 999078 Macao Peoples R China

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults, and analyzing retinal fundus plays a crucial role in early DR screening. However, multi-lesion segmentation of fundus images remains a challenging task due to the highly diverse shape, size, position, and brightness of lesions. In this paper, we propose an interpretable network framework for multi-lesion segmentation by integrating the prior knowledge of retinal fundus images into it. Specifically, we propose a probabilistic model for retinal fundus images, which is in a foreground-background decomposition manner, and both the characteristics of the foreground (lesions) and background (non-lesion fundus images) are taken into consideration. Then, we exploit the expectation- maximization (EM) algorithm to solve the proposed model and design a novel network architecture under the guidance of the calculation flow of the algorithm, called Decomposition-Segmentation Network (DS-Net). The components of the network consist of two subnetworks: S-Net and D-Net, corresponding to the E step and M step of the EM algorithm, respectively. D-Net aims to decompose the background and foreground compositions of the fundus image and S-Net takes the foreground as input and executes segmentation task. Similar to the EM algorithm, the two subnetworks separate the original segmentation task into two much easier but interpretable sub-tasks. This not only helps enhance the performance, but also greatly facilitates a deeper analysis of the network. Moreover, the proposed DS-Net framework can be easily integrated with current lesion segmentation networks in a plug-and-play manner, by setting current segmentation networks as S-Net in the framework, and lead to general performance improvements. Experiments on benchmark datasets substantiate the superiority of DS-Net quantitatively and visually. The code of our method is available at https://***/tanfy929/DS-Net.

关键词： Multi-class segmentation Prior knowledge of retinal fundus images expectation-maximization algorithm Interpretable network Deep learning

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Majorization ordering of dependent aggregate claims clustered by statistical machine learning

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Nevruz, Ezgi Yildirak, Kasirga Sengupta, Ashis Hacettepe Univ Dept Actuarial Sci Ankara Turkiye MCG Augusta Univ Dept Populat Hlth Sci Augusta GA USA Indian Inst Technol Dept Math Kharagpur India Middle East Tech Univ Dept Stat Ankara Turkiye

The primary driver of decision-making is prioritization or ordering of risks, which plays a vital role optimizing risk management strategies. This paper focuses on ordering aggregate claim vectors across various risk clusters utilizing agricultural insurance data. The data was sourced from the Turkish Agricultural Insurance Pool (TARS & Idot;M), the sole entity responsible for compiling agricultural insurance claim datasets. We consider the spatial and temporal features of claims, supposing that individual claims subject to similar environmental risks are dependent. We cluster risks based on meteorological values related to the location and time the reported crop-hail insurance claims, estimated using an extended spatiotemporal interpolation method that we proposed. Bayesian regularization enhanced the performance of the statistical machine learning approach. Having clustered the risk regions, we order the aggregate claim vectors by using majorization relation and Schur-convex risk measures, which are more flexible for multivariate actuarial risks. Moreover, a contribution to the literature, we modify the definition of majorization to fulfill the criteria for continuous random variables. The findings of this study indicate that the risk clusters, when ordered according to the modified majorization conditions and the Schur-convex risk measure, exhibit consistency. These results further demonstrate the compatibility of the climate-based, probabilistic clustering method with the modified majorization relation.

关键词： Agricultural insurance expectation-maximization algorithm Multivariate actuarial risk Partial order theory Schur-convexity

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The dynamic expectation-conditional maximization either' algorithm

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2012年第2期74卷 313-336页

作者： He, Yunxiao Liu, Chuanhai Purdue Univ Dept Stat W Lafayette IN 47907 USA Yale Univ New Haven CT USA

. The expectationconditional maximization either (ECME) algorithm has proven to be an effective way of accelerating the expectationmaximization algorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME implementation is what we call DECME-1, which uses the line that is determined by the two most recent estimates as the acceleration subspace. The investigation of DECME-1 leads to an efficient, simple, stable and widely applicable DECME implementation, which uses two-dimensional acceleration subspaces and is referred to as DECME-2. The fast convergence of DECME-2 is established by the theoretical result that, in a small neighbourhood of the maximum likelihood estimate, it is equivalent to a conjugate direction method. The remarkable accelerating effect of DECME-2 and its variant is also demonstrated with several numerical examples.

关键词： Conjugate direction expectation-conditional maximization algorithm expectation-conditional maximization either algorithm expectation-maximization algorithm Square iterative methods Successive over-relaxation

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Modelling the size distribution and bioaccumulation of gold nanoparticles under mixture exposure

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AQUATIC TOXICOLOGY 2025年 280卷 107286页

作者： Li, Yao Wang, Xiangrui Liang, Dingyuan Zhao, Xiaoli Dong, Zhaomin Bai, Yingchen Wang, Wen-Xiong Peijnenburg, Willie J. G. M. Wang, Ying Fan, Wenhong Beihang Univ Sch Mat Sci & Engn Beijing 100191 Peoples R China Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China City Univ Hong Kong State Key Lab Marine Pollut Kowloon Hong Kong Peoples R China Leiden Univ Inst Environm Sci CML NL-2300 RA Leiden Netherlands Natl Inst Publ Hlth & Environm RIVM Ctr Safety Subst & Prod NL-3720 BA Bilthoven Netherlands

To assess the bioaccumulation and toxicity of nanoparticles (NPs), analyzing and modelling the relationship between the size distribution of NPs in organisms and the exposure particle size distribution represents an important challenge. Previous studies mostly focused on the NPs with single size. However, the size distribution of NPs is wide and variable in the natural environment. There is a lack of research on the NPs with mixed sizes. This study investigated the size distribution of three gold (Au) NPs with different sizes and their mixtures within a ciliate Tetrahymena thermophila under the same number concentration of particles. Results revealed that smaller particles tended to aggregate and bioaccumulate more in cells. Using expectation-maximization algorithm, a particle size distribution model of NPs in cells was established. This model effectively simulated the size distribution of NPs with mixed sizes in cells, demonstrating high accuracy with a mean absolute error of < 0.001, a root mean squared error of < 0.001, and a correlation coefficient exceeding 0.98. Experimental results further verified that the model reliably predicted the size distribution of NPs with mixed sizes in cells, and smaller particles accounted for a larger proportion of the size distribution and bioaccumulation. These results demonstrated the importance of particle size and size distribution of NPs in their environmental effects. Models developed here can provide guidance for future evaluation of the environmental risks of NPs mixtures.

关键词： Nanoparticles Single-particle ICP-MS Size distribution expectation-maximization algorithm Tetrahymena thermophila

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On a class of finite mixture models that includes hidden Markov models

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JOURNAL OF MULTIVARIATE ANALYSIS 2025年 208卷

作者： Bartolucci, Francesco Pandolfi, Silvia Pennoni, Fulvia Univ Perugia Dipartimento Econ Via A Pascoli 20 I-06123 Perugia Italy Univ Milano Bicocca Dipartimento Stat & Metodi Quantitat Via Bicocca Arcimboldi 8 I-20126 Milan Italy

In the context of longitudinal data, we introduce a class of finite mixture (FM) models that generalizes that of hidden Markov (HM) models, and derive conditions under which the two classes are equivalent. On the basis of this result, we develop a likelihood ratio (LR) misspecification test for assessing the latent structure of an HM model, along with a multiple version of this test that may be used in the presence of many latent states or time occasions. This testing procedure requires the maximum likelihood estimation of the two models under comparison, that is, the assumed HM model and the more general FM model, which is performed by suitable versions of the expectation-maximization algorithm. The approach is validated through a simulation study, aimed at assessing the performance of the proposed tests under different circumstances, and by an application using data derived from the SCImago Journal & Country Rank database.

关键词： expectation-maximization algorithm Likelihood ratio test Model selection Multiple testing SCImago Journal & Country Rank

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Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2025年第1期34卷 222-238页

作者： Sidrow, Evan Heckman, Nancy Bouchard-Cote, Alexandre Fortune, Sarah M. E. Trites, Andrew W. Auger-Methe, Marie Univ British Columbia Dept Stat Vancouver BC Canada Dalhousie Univ Dept Oceanog Halifax NS Canada Univ British Columbia Inst Oceans & Fisheries Dept Zool Vancouver BC Canada Univ British Columbia Inst Oceans & Fisheries Dept Stat Vancouver BC Canada

Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large datasets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying dataset for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire dataset. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs, with less computation time, and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series datasets more efficiently than existing baselines. Supplemental materials are available online.

关键词： expectation-maximization algorithm Maximum likelihood estimation State space model Statistical ecology Stochastic gradient descent

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The EM-BDD algorithm For Learning Hidden Markov Models 12th

The EM-BDD Algorithm For Learning Hidden Markov Models

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12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Gunnarsdottir, Eva Osk Ingolfsdottir, Anna Reykjavik Univ Reykjavik Iceland

ISBN: (纸本)9783031751066;9783031751073

We introduce the EM-BDD algorithm, a novel approach to learning Hidden Markov Models (HMMs) by leveraging Binary Decision Diagrams (BDDs). HMMs are pivotal in representing complex systems revolutionizing fields such as autonomous vehicles and language technology. However, the Baum-Welch (BW) algorithm, the state-of-the-art solution for learning HMMs, is hampered by its high computational complexity in each iteration, limiting its efficiency and scalability. The EM-BDD algorithm addresses these challenges by utilizing BDDs in the Forward-Backward procedure, building upon the BW algorithm's methodology. This approach significantly affects the algorithm's time and memory complexity, contingent on the BDD's size, which in turn is highly dependent on variable ordering - an NP-complete problem. Our evaluation through a comparative analysis between the EM-BDD and BW algorithms demonstrates the former's potential for enhanced efficiency and scalability in learning HMMs.

关键词： Binary Decision Diagrams Hidden Markov Models expectation-maximization algorithm

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BLIND AUDIO SOURCE SEPARATION USING TWO expectation-maximization algorithmS 30

BLIND AUDIO SOURCE SEPARATION USING TWO EXPECTATION-MAXIMIZA...

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30th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Eisenberg, Aviad Schwartz, Boaz Gannot, Sharon Bar Ilan Univ Fac Engn Ramt Gan Israel

ISBN: (纸本)9781728166629

The problem of multi-microphone blind audio source separation in noisy environment is addressed. The estimation of the acoustic signals and the associated parameters is carried out using the expectation-maximization algorithm. Two separation algorithms are developed using either deterministic representation or stochastic Gaussian distribution for modelling the speech signals. Under the deterministic model, the speech sources are estimated in the M-step by applying in parallel multiple minimum variance distortionless response (MVDR) beamformers, while under the stochastic model, the speech signals are estimated in the E-step by applying in parallel multiple multichannel Wiener filters (MCWF). In the simulation study, we generated a large dataset of microphone signals, by convolving speech signals, with overlapping activity patterns, by measured acoustic impulse responses. It is shown that the proposed methods outperform a baseline method in terms of speech quality and intelligibility.

关键词： Blind audio source separation expectation-maximization algorithm MVDR and multichannel Wiener filter beamforming

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Accurate force evaluation in prestressed cable-strut structures: A robust sparse Bayesian learning method with feedback-driven error optimization

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ENGINEERING STRUCTURES 2025年 330卷

作者： Chen, Yao Zhou, Haodong Gao, Jiangjun Shen, Zhengliang Xie, Tianyu Sareh, Pooya Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Sch Civil Engn Wuxi Campus Wuxi 214082 Peoples R China China Construct Fifth Engn Bur Co Ltd Changsha 410004 Peoples R China Newcastle Univ Sch Engn Creat Design Engn Lab Cdel Newcastle Upon Tyne NE1 7RU England Univ Politecn Madrid UPM Escuela Tecn Super Ingn & Diseno Ind Madrid 28012 Spain

Force evaluation is critical to ensuring the safety of cable-strut structures during service. This study employs dynamic testing to assess the internal forces resulting from cable relaxation in prestressed cable-strut structures. A cross-model cross-mode algorithm is utilized to establish a cable force evaluation model. This approach broadens the range of available modes and addresses mismatches between modes before and after cable force loss. To enhance the accuracy and reliability of the force evaluation, a robust sparse Bayesian learning method is proposed. Measurement noise is modeled as a mixture of Gaussian distributions rather than a single Gaussian distribution, enabling a more precise representation of uncertainties in force evaluation. Furthermore, a feedback-driven error optimization process is introduced to minimize residuals through multiple linear iterations. Numerical simulations demonstrate that the proposed method achieves greater evaluation accuracy compared to existing sparse Bayesian approaches. Comparative analyses under varying noise levels reveal that the proposed method is robust and effectively reduces the impact of measurement noise.

关键词： Force evaluation Prestressed cable-strut structure Sparse Bayesian learning expectation-maximization algorithm Uncertainty quantification

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