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Tikhonov regularization for Gaussian empirical gain maximization in RKHS is consistent

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2025年 76卷

作者： Feng, Yunlong Wu, Qiang SUNY Albany Dept Math & Stat Albany NY 12222 USA Univ Tennessee Dept Math Knoxville TN USA

Without imposing light-tailed noise assumptions, we prove that Tikhonov regularization for Gaussian Empirical Gain maximization (EGM) in a reproducing kernel Hilbert space is consistent and further establish its fast exponential type convergence rates. In the literature, Gaussian EGM was proposed in various contexts to tackle robust estimation problems and has been applied extensively in a great variety of real-world applications. A reproducing kernel Hilbert space is frequently chosen as the hypothesis space, and Tikhonov regularization plays a crucial role in model selection. Although Gaussian EGM has been studied theoretically in a series of papers recently and has been well-understood, theoretical understanding of its Tikhonov regularized variants in RKHS is still limited. Several fundamental challenges remain, especially when light- tailed noise assumptions are absent. To fill the gap and address these challenges, we conduct the present study and make the following contributions. First, under weak moment conditions, we establish a new comparison theorem that enables the investigation of the asymptotic mean calibration properties of regularized Gaussian EGM. Second, under the same weak moment conditions, we show that regularized Gaussian EGM estimators are consistent and further establish their fast exponential-type convergence rates. Our study justifies its feasibility in tackling robust regression problems and explains its robustness from a theoretical viewpoint. Moreover, new technical tools including probabilistic initial upper bounds, confined effective hypothesis spaces, and novel comparison theorems are introduced and developed, which can faciliate the analysis of general regularized empirical gain maximization schemes that fall into the same vein as regularized Gaussian EGM.

关键词： expectation maximization algorithm

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia Computer Science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network expectation maximization algorithm Natural language processing

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Track Misregistration Estimation Technique based on Hybrid K-means and em algorithm in Bit-Patterned Media Recording Systems

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IEEE Transactions on Magnetics 2025年

作者： Kochcha, Pijit Kankhunthod, Kittipon Warisarn, Chanon King Mongkut's Institute of Technology Ladkrabang College of Advanced Manufacturing Innovation Bangkok10520 Thailand

Track misregistration (TMR) in ultra-high density bit-patterned media recording (BPMR) is a significant issue, severely degrading system performance. Although TMR can be managed by a servo control loop, this paper proposes a hybrid TMR mitigation method based on K-means and expectation-maximization (EM) algorithms to enhance TMR prediction accuracy and improve bit-error-rate (BER) in multi-head/multi-track BPMR systems. This method utilizes two-dimensional (2-D) equalizer and one-dimensional (1-D) generalized partial response (GPR) target coefficients for the soft-output Viterbi algorithm (SOVA) detector according to the estimated TMR level to mitigate this effect. Simulation results demonstrate that the proposed system significantly outperforms conventional systems, especially under high TMR conditions. The hybrid approach achieves high TMR estimation accuracy and delivers BER performance close to an ideal system with perfect TMR estimation, showing up to a 1.25 dB improvement in BER over systems without TMR mitigation. These findings underscore the effectiveness of the hybrid K-means-EM-based TMR estimator in enhancing system performance under various conditions. © 1965-2012 IEEE.

关键词： expectation maximization algorithm

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Robust online identification method for biofabrication processes with multiple unknown disturbances

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Journal of the Franklin Institute 2025年第7期362卷

作者： Chu, Yixuan Ping, Xiaojing Zhao, Shunyi Liu, Fei Jiangnan University Wuxi214122 China

This paper addresses the challenge of online parameter identification for biofabrication processes with multiple sensors, particularly under unknown disturbances. A robust recursive multitask expectation maximization (RMTEM) algorithm is proposed within Bayesian framework. The algorithm integrates data of multi-sensor to recursively estimate both unknown noise variances and system parameters, ensuring adaptability to plug-and-play sensors and real-time applications. By leveraging information from heterogeneous noise sources, the RMTEM algorithm exhibits enhanced robustness and adaptability to fluctuating disturbances. Numerical simulations demonstrate its superior identification accuracy compared to existing methods, while a continuous fermenter case further validates its effectiveness and practical relevance in complex biofabrication scenarios. © 2025

关键词： expectation maximization algorithm

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Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs

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IEEE Transactions on Communications 2025年

作者： Schmid, Luca Raviv, Tomer Shlezinger, Nir Schmalen, Laurent Hertzstr. 16 Karlsruhe76187 Germany Ben-Gurion University of the Negev School of ECE Be'er-Sheva Israel

We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In particular, we consider the expectation maximization (EM) algorithm for maximum likelihood estimation, which typically suffers from high complexity as it requires the computation of the symbol-wise posterior distributions in every iteration. We address this issue by efficiently approximating the posteriors using the belief propagation (BP) algorithm on a suitable factor graph. By interweaving the iterations of BP and EM, the detection complexity can be further reduced to a single BP iteration per EM step. In addition, we propose a data-driven version of our algorithm that introduces momentum in the BP updates and learns a suitable EM parameter update schedule, thereby significantly improving the performance-complexity tradeoff with a few offline training samples. Our numerical experiments demonstrate the excellent performance of the proposed blind detector and show that it even outperforms coherent BP detection in high signal-to-noise scenarios. © 2025 IEEE.

关键词： expectation maximization algorithm

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Efficient Influential Nodes Tracking via Link Prediction in Evolving Networks

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IEEE Internet of Things Journal 2025年

作者： Zhang, Kexin Cai, Taotao Liu, Zhaoyu Teng, Shuang Wang, Yi Chen, Yu Zhang, Ji Zhengzhou University of Aeronautics School of Electronics and Information Henan Zhengzhou450046 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Henan Yuanfang Human Digital Technology Service Group Co. Ltd. Henan Zhengzhou450000 China

Influence maximization (IM), which aims to identify the most influential k nodes in a network, is fundamental to numerous applications, including viral marketing and recommendation systems. This topic has garnered significant scholarly attention. However, most existing research addresses the IM problem in static networks, neglecting the dynamic and continually evolving nature of social networks. In this paper, we introduce a novel problem: Influential Nodes Tracking in Future Networks (INTFN). The INTFN problem aims to quickly find the most influential k nodes in networks over upcoming time intervals. We formally define the INTFN problem and prove its NP-hardness. To address this challenge, we propose a comprehensive solution that predicts the future structure of social networks using a carefully selected link prediction technique. Subsequently, we identify the most influential k nodes in these future networks by employing classic IM algorithms. Additionally, we design a dictionary structure and propose the Compressed Subgraphs based Influential Nodes Tracking (CSINT) algorithm to enhance the efficiency of our solution. Extensive experiments on four real-world datasets demonstrate the effectiveness and efficiency of the proposed CSINT algorithm. © 2014 IEEE.

关键词： expectation maximization algorithm

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Mixture Density Function Estimation in Shape Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Iwata, Kazunori Hiroshima City University Graduate School of Information Sciences Hiroshima731-3194 Japan

Recent developments in measurement tools have made it easier to obtain shape data, a collection of point coordinates in vector space that are meaningful when some of them are gathered together. As a result, clustering of shape data becomes increasingly important. However, few studies still perform applicable clustering in various cases because some studies rely on their specific shape representations. Thus, we apply a simple and widely recognized representation and generative model to shape. A configuration matrix of the point coordinates is used for the representation, and it is the simplest and most well-accepted representation in conventional shape analysis. As a generative model, we consider the mixture density function, a well-known model in statistics for expressing a population density function, which is a linear combination of subpopulation density functions. The aim of this paper is to present a mixture density-based model that will be useful for clustering shape data. The clustering of shapes involves estimating the parameters of the model, and this estimation is derived using an EM algorithm based on the model. As examples of promising shape-data applications, the computational analyses of ape skulls, American football formations, and baseball pitches were performed. In addition, we evaluated the performance of the EM algorithm by comparing it with other typical clustering methods. The theoretical results not only contribute to statistical estimation for shape data but also extend the clustering of non-vector shape data. The experimental results show that the derived EM algorithm performs well in shape clustering. © 2025 IEEE.

关键词： expectation maximization algorithm

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Uncertainty-Aware Graph Self-Training with expectation-maximization Regularization

arXiv

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arXiv 2025年

作者： Wang, Emily Chen, Michael Li, Chao School of Information Technology Lakeside College Ohio United States Department of Applied Mathematics North River University Manchester United Kingdom Department of Computer Science Eastern Asia Institute of Technology Beijing China

In this paper, we propose a novel uncertainty-aware graph self-training approach for semi-supervised node classification. Our method introduces an expectation-maximization (EM) regularization scheme to incorporate an uncertainty mechanism during pseudo-label generation and model retraining. Unlike conventional graph self-training pipelines that rely on fixed pseudo-labels, our approach iteratively refines label confidences with an EM-inspired uncertainty measure. This ensures that the predictive model focuses on reliable graph regions while gradually incorporating ambiguous nodes. Inspired by prior work on uncertainty-aware self-training techniques [5], our framework is designed to handle noisy graph structures and feature spaces more effectively. Through extensive experiments on several benchmark graph datasets, we demonstrate that our method outperforms strong baselines by a margin of up to 2.5% in accuracy while maintaining lower variance in performance across multiple runs. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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CSI-Based NOMA with em algorithm for ISAC

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IEEE Transactions on Vehicular Technology 2025年

作者： Zheng, Qi Wen, Miaowen Xiao, Yue Li, Jie South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China

Channel state information (CSI)-based sensing can interfere with communication signals in existing wireless networks. To address this and achieve integrated sensing and communication (ISAC) with higher spectral efficiency, in this letter, we propose a novel expectation maximization (EM) algorithm-aided non-orthogonal multiple access (NOMA) scheme that enables a communication user and a sensing user to share same time and frequency resources, and extend it to multi-input multi-output (MIMO) scenarios. By using the EM algorithm, communication CSI, sensing CSI, and data symbols are iteratively estimated. Moreover, we introduce a new sensing pattern for highly accurate initial estimation of the communication channel, such that enhanced sensing channel estimation subsequently refines communication channel estimation. The initial sensing channel estimation performance is theoretically analyzed. Simulation results demonstrate the superiority of our approach for both phase shift keying (PSK) and quadrature amplitude modulation (QAM) signals over the benchmark scheme. © 1967-2012 IEEE.

关键词： expectation maximization algorithm

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Clustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Correction

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IEEE Transactions on Vehicular Technology 2025年

作者： Haque, Fahimul Dehghanian, Vahid Fapojuwo, Abraham O. Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada

With the rise of autonomous and semi-autonomous vehicles, effective fault detection and mitigation (FDM) methods have become essential in meeting the integrity requirements for precise and reliable Global Navigation Satellite System (GNSS)-based positioning. The existing GNSS-only FDM methods are ineffective in scenarios involving multiple faulty observations due to either their theoretical model limitations or high computational costs. In this paper, a novel GNSS multi-fault detection and correction method is developed that achieves a balance between computational complexity and performance. The proposed method incorporates an expectation maximization (EM) framework to jointly estimate an approximate maximum likelihood of states and latent model parameters in the presence of observation outliers, i.e., faults. However, the EM algorithm is known for its high computational complexity. To reduce the computational complexity of EM, an importance sampling step based on unsupervised clustering is introduced. As demonstrated by the results and analysis herein, the proposed method outperforms the existing Least-squares Residuals method, achieving an average improvement of up to 48% in positioning accuracy. Additionally, the computational complexity of the proposed method is an order of magnitude lower than the state-of-the-art Solution Separation method. The method enhances positioning reliability at a lower computational cost and does not require additional infrastructure, hence, can be readily integrated into standalone real-time GNSS applications. © 1967-2012 IEEE.

关键词： expectation maximization algorithm

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