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Design and analysis of direction of arrival using hybrid expectation-maximization and MUSIC for wireless communication

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OPTIK 2018年 170卷 48-55页

作者： Malla, Prince Priya Mukherjee, Amrit Routray, Sidheswar Palai, G. KL Univ Dept Elect & Commun Engn Guntur Andhra Prades India KIIT Univ Sch Elect Engn Bhubaneswar Odisha India GITA Dept Elect & Commun Engn Bhubaneswar Odisha India

As multiple signal classification (MUSIC) algorithm is unable to estimate the direction of arrival (DOA) of highly correlated or coherent signal, based on array processing and decomposition of covariance matrix of incident signals due to its lack of source identification. In the proposed hybrid model, we have considered expectation maximization (EM) algorithm for precise identification of the DOA of highly correlated signals in wireless communication applications. The proposed model is analysed, simulated and verified using two different source signals. The mathematical analysis shows substantial closeness with the simulated results. The robustness of the proposed algorithm is further verified by adding noise to the incident signals. The utilization of the EM algorithm in the proposed hybrid approach reduces the time requirement and mathematical complexity of MUSIC algorithm for DOA estimation. By exploiting the EM algorithm, the original transmitted signal and its arriving angle on the antenna element are estimated from the mixture of interferer's signal, transmitted signal, multipath component and noise. Hence it is straightforward to recognise the desired signal.

关键词： Direction of arrival Music algorithm expectation maximization algorithm Wireless communication

HYPERGRAPH LINK PREDICTION VIA HYPEREDGE COPYING

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arXiv 2025年

作者： He, Xie Chodrow, Philip S. Mucha, Peter J. Microsoft Research United States Middlebury College United States Dartmouth College United States

We propose a generative model of temporally-evolving hypergraphs in which hyperedges form via noisy copying of previous hyperedges. Our proposed model reproduces several stylized facts from many empirical hypergraphs, is learnable from data, and defines a likelihood over a complete hypergraph rather than ego-based or other sub-hypergraphs. Analyzing our model, we derive descriptions of node degree, edge size, and edge intersection size distributions in terms of the model parameters. We also show several features of empirical hypergraphs which are and are not successfully captured by our model. We provide a scalable stochastic expectation maximization algorithm with which we can fit our model to hypergraph data sets with millions of nodes and edges. Finally, we assess our model on a hypergraph link prediction task, finding that an instantiation of our model with just 11 parameters can achieve competitive predictive performance with large neural networks. © 2025, CC BY.

关键词： expectation maximization algorithm

Priority-Based Weighted Constrained Crowd Judgement Analysis with Quantum Genetic algorithm 4th

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Priority-Based Weighted Constrained Crowd Judgement Analysis...

4th International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Mandal, Suraj Chatterjee, Sujoy Mukhopadhyay, Anirban University of Kalyani Kalyani India University of Campinas Sao Paulo Brazil Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9783031837920

Crowdsourcing has already obtained a lot of attention from researchers due to the enormous power of solving complex problems in less time and at a minimal cost. Most of the research considers finding aggregated judgment from multiple diverse opinions of workers, and several algorithms such as majority-voting, weighted majority voting, and expectation-maximization (EM) algorithms are widely accepted. Recently, a relatively unexplored area of judgement analysis algorithm was introduced in the literature, and each crowd worker provides their opinions that comprise multiple components for a single question, and there is a constraint for each pair of components. However, in state-of-the-art approaches, the weight of the opinions is not considered. In this work, a new variant of the problem is introduced, while the weights between the components are considered. More importantly, the weights are not predefined, rather they are collected from the crowd workers. Thus, it can help us to explore many other solutions based on the users’ preferences which is very difficult to obtain from decision-makers if the crowdsourcing technique is not employed. Quantum computing has been a new approach to computer science that explores the idea of parallelism in computing. In this work, we have introduced a quantum genetic algorithm that explores solving the multiple objective-based weighted constrained crowd judgment analysis. In addition to that, we have introduced a new variant of constrained crowd judgment analysis that not only explores the solution to the multiple opinion-based solutions but also makes a better interpretation of the solutions by including textual information and additional weight parameters. The algorithm is applied to a real-life crowdsourced dataset obtained from the campus of the University of Kalyani. The experimental results demonstrate the applicability of the proposed method while providing a better solution compared to the traditional method. © The Author(s),

关键词： expectation maximization algorithm

Cluster weighted models for functional data

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arXiv 2025年

作者： Anton, Cristina Smith, Iain Department of Mathematics and Statistics MacEwan University 10700 – 104 Avenue EdmontonABT5J 4S2 Canada

We propose a method, funWeightClust, based on a family of parsimonious models for clustering heterogeneous functional linear regression data. These models extend cluster weighted models to functional data, and they allow for multivariate functional responses and predictors. The proposed methodology follows the approach used by the the functional high dimensional data clustering (funHDDC) method. We construct an expectation maximization (EM) algorithm for parameter estimation. Using simulated and benchmark data we show that funWeightClust outperforms funHDDC and several two-steps clustering methods. We also use funWeightClust to analyze traffic patterns in Edmonton, Canada. © 2025, CC BY-NC-ND.

关键词： expectation maximization algorithm

Social Welfare maximization in Approval-Based Committee Voting under Uncertainty

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arXiv 2025年

作者： Aziz, Haris Guo, Yuhang Kagita, Venkateswara Rao Rastegari, Baharak Suzuki, Mashbat University of New South Wales Sydney Australia National Institute of Technology Warangal India University of Southampton Southampton United Kingdom

Approval voting is widely used for making multi-winner voting decisions. The canonical rule (also called Approval Voting) used in the setting aims to maximize social welfare by selecting candidates with the highest number of approvals. We revisit approval-based multi-winner voting in scenarios where the information regarding the voters’ preferences is uncertain. We present several algorithmic results for problems related to social welfare maximization under uncertainty, including computing an outcome that is social welfare maximizing with the highest probability, computing the social welfare probability distribution of a given outcome, computing the probability that a given outcome is social welfare maximizing, and understanding how robust an outcome is with respect to social welfare maximizing. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

DATA DENOISING WITH SELF CONSISTENCY, VARIANCE maximization, AND THE KANTOROVICH DOMINANCE

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arXiv 2025年

作者： Hiew, Joshua Zoen-Git Lim, Tongseok Pass, Brendan de Souza, Marcelo Cruz Department of Mathematical and Statistical Sciences University of Alberta EdmontonAB Canada Mitch Daniels School of Business Purdue University West LafayetteIN47907 United States Financial System Monitoring Department Central Bank of Brazil CE Fortaleza Brazil

We introduce a new framework for data denoising, partially inspired by martingale optimal transport. For a given noisy distribution (the data), our approach involves finding the closest distribution to it among all distributions which 1) have a particular prescribed structure (expressed by requiring they lie in a particular domain), and 2) are self-consistent with the data. We show that this amounts to maximizing the variance among measures in the domain which are dominated in convex order by the data. For particular choices of the domain, this problem and a relaxed version of it, in which the self-consistency condition is removed, are intimately related to various classical approaches to denoising. We prove that our general problem has certain desirable features: solutions exist under mild assumptions, have certain robustness properties, and, for very simple domains, coincide with solutions to the relaxed problem. We also introduce a novel relationship between distributions, termed Kantorovich dominance, which retains certain aspects of the convex order while being a weaker, more robust, and easier-to-verify condition. Building on this, we propose and analyze a new denoising problem by substituting the convex order in the previously described framework with Kantorovich dominance. We demonstrate that this revised problem shares some characteristics with the full convex order problem but offers enhanced stability, greater computational efficiency, and, in specific domains, more meaningful solutions. Finally, we present simple numerical examples illustrating solutions for both the full convex order problem and the Kantorovich dominance problem. © 2025, CC BY.

关键词： expectation maximization algorithm

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

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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

FLEXIBLE BIVARIATE BETA MIXTURE MODEL: A PROBABILISTIC APPROACH FOR CLUSTERING COMPLEX DATA STRUCTURES

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arXiv 2025年

作者： Hsu, Yung-Peng Chen, Hung-Hsuan National Central University Taiwan

Clustering is essential in data analysis and machine learning, but traditional algorithms like k-means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To address the challenge, we introduce the Flexible Bivariate Beta Mixture Model (FBBMM), which utilizes the flexibility of the bivariate beta distribution to handle diverse and irregular cluster shapes. Using the expectation maximization (EM) algorithm and Sequential Least Squares Programming (SLSQP) optimizer for parameter estimation, we validate FBBMM on synthetic and real-world datasets, demonstrating its superior performance in clustering complex data structures, offering a robust solution for big data analytics across various domains. We release the experimental code at https://***/yung-peng/MBMM-and-FBBMM. © 2025, CC BY.

关键词： expectation maximization algorithm

Modeling discrete common-shock risks through matrix distributions

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arXiv 2025年

作者： Bladt, Martin Cheung, Eric C.K. Peralta, Oscar Woo, Jae-Kyung Department of Mathematical Sciences University of Copenhagen Copenhagen Denmark School of Risk and Actuarial Studies UNSW Business School University of New South Wales Sydney Australia Department of Actuarial and Insurance Sciences Autonomous Technological Institute of México México City Mexico

We introduce a novel class of bivariate common-shock discrete phase-type (CDPH) distributions to describe dependencies in loss modeling, with an emphasis on those induced by common shocks. By constructing two jointly evolving terminating Markov chains that share a common evolution up to a random time corresponding to the common shock component, and then proceed independently, we capture the essential features of risk events influenced by shared and individual-specific factors. We derive explicit expressions for the joint distribution of the termination times and prove various class and distributional properties, facilitating tractable analysis of the risks. Extending this framework, we model random sums where aggregate claims are sums of continuous phase-type random variables with counts determined by these termination times, and show that their joint distribution belongs to the multivariate phase-type or matrix-exponential class. We develop estimation procedures for the CDPH distributions using the expectation-maximization algorithm and demonstrate the applicability of our models through simulation studies and an application to bivariate insurance claim frequency data. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm