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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Wang, Tianyi Sun, Wenxu Shima, Keisuke Yokohama Natl Univ Inst Multidisciplinary Sci 79-7 TokiwadaiHodogaya Ku Yokohama Kanagawa 2408501 Japan Kyoto Univ Grad Sch Engn Dept Elect Engn Nishikyo Kyoto 6158510 Japan Kyoto Univ Lab Innovat Tech Infrastruct 1-30 GoryooharaNishikyo Ku Kyoto 6158245 Japan Yokohama Natl Univ Fac Environm & Informat Sci 79-7 TokiwadaiHodogaya Ku Yokohama Kanagawa 2408501 Japan

This study presents a novel approach to monitoring respiration rate using frequency modulated continuous wave (FMCW) millimeter-wave radar, aiming to overcome the limitations of traditional non-contact methods. Fourteen participants were involved in an experiment that included alternating periods of stillness and left-right continuous body movement. Data analysis involved non-negative matrix factorization (NMF) and template matching (TM) techniques. Respiration measured by millimeter-wave radar was compared with ground truth measurements. Results demonstrated accurate respiration detection even during continuous body movement, with a significant correlation between radar-based measurements and ground truth data. This study pioneers radar-based respiration monitoring under continuous body movement conditions, offering promising implications for practical applications.

关键词： Millimeter-wave radar Vital signal non-contact detection Body movement robust non-negative matrix factorization

Bearing fault signal detection based on non-negative matrix factorization and dual-feedback segmented unsaturated tristable stochastic resonance system

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026115-026115页

作者： Zhang, Gang Cao, Longmei Chongqing Univ Posts & Telecommun CQUPT Sch Commun & Informat Engn Chongqing 400065 Peoples R China

Stochastic resonance is widely used in bearing fault detection due to its ability to enhance weak signals. This paper proposes a fault detection method that combines noise reduction with stochastic resonance. Firstly, a segmented unsaturated potential function based on the classic potential function is constructed, and a dual-feedback structure is introduced to feed the system output back to the input, thereby enhancing system performance. Secondly, the theoretical expressions for the mean first passage time and the spectral amplification of the dual-feedback segmented unsaturated tristable stochastic resonance (DSUTSR) system are derived and analyzed. Additionally, a numerical simulation comparison using the fourth-order Runge-Kutta method is performed between the DSUTSR system and its predecessor systems to verify the improvements brought by the dual-feedback structure. Subsequently, non-negative matrix factorization (NMF) is introduced as a noise reduction method, with cross-validation used to determine the decomposition rank of NMF to guide the decomposition of the fault signal matrix. Finally, the combination of NMF and the DSUTSR system is used to detect bearing fault frequencies under white noise and L & eacute;vy noise backgrounds. Experimental results demonstrate the superiority and effectiveness of the proposed method in fault signal detection. This system holds significant potential for future weak signal detection, effectively enhancing and identifying fault signals hidden within noisy backgrounds.

关键词： stochastic resonance bearing fault detection dual-feedback structure non-negative matrix factorization

Attributed Network Clustering through Mutual Affinity Propagated non-negative matrix factorization

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SN Computer Science 2025年第5期6卷 1-17页

作者： Pahari, Saikat Dey, Paramita Department of Computer Science Engineering OmDayal College of Engineering and Architecture West Bengal Howrah 711316 India Department of Information Technology Government College of Engineering and Ceramic Technology West Bengal Kolkata 700010 India

Attributed Network Clustering (ANC) has garnered significant attention in research for identifying communities within a complex network like a social, biological, or information network. Generally, such networks are represented as a graph where the nodes denote entities and edges are relationships between two adjacent nodes. In real-world networks, nodes are associated with attributes to express their properties. In this context, a community is defined as a group of nodes that interact more frequently with each other than with nodes outside their group. ANC involves creating a mapping function that converts nodes and associated attributes into lower-dimensional vector representations. Once transformed into these vectors, various machine learning algorithms are used to cluster the nodes and reveal the underlying communities. However, most ANC methods confront two major challenges. First, they assimilate node attribute information from the local neighborhood, as a result, they can’t explore the mutual affinity between node and attribute. Second, they do not consider the values of the community membership of the nodes at the time of clustering. This leads to poor performance, as the embedding method and clustering methods are independent. To address this, this article proposes a method named Mutual Affinity Propagation-based nonnegative matrix factorization (MAP-NMF). The proposed method works in two phases. In the first phase, an attribute-augmented transition probability matrix is constructed to capture the mutual affinity between the node and attribute and obtain node representation using singular value decomposition of the same. The second phase produces community membership from the obtained latent representation through an NMF-based joint optimization. Moreover, a novel updating rule is proposed for the joint optimization method. Empirical study on both real-world and artificial networks proves the superiority of our proposed algorithm. © The Author(s), under exc

关键词： Attribute augmented graph Attributed network embedding Joint optimization non-negative matrix factorization

Dual graph-regularized sparse robust adaptive non-negative matrix factorization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 281卷

作者： Xiong, Weizhi Ma, Yanrong Zhang, Cheng Liu, Sheng North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Sch Preparatory Educ Yinchuan 750021 Peoples R China Gandong Univ Dept Basic Educ Fuzhou 344000 Jiangxi Peoples R China Tongren Univ Sch Date Sci Tongren 554300 Guizhou Peoples R China

This paper introduces a sparse non-negative matrix factorization approach that integrates robust estimators with dual graph learning. This method boasts robustness and effectively segregates samples from different classes while clustering those within the same class. Specifically, a robust estimator is employed to ensure that normal samples dominate the modeling process, assigning lower weights to outliers to mitigate their influence. Recognizing the potential for traditional squared L-2-norm to amplify outliers, we adopt a q-order (1 <= q <= 2) of L-2-norm to refine error measurement, enhancing model performance. Subsequently, a similarity matrix is constructed leveraging stable adaptive spectral clustering and cosine similarity, with dual graph learning techniques, independent of unknown parameters, applied to deeply explore the local, global, and manifold structures of the data, particularly adept at handling complex nonlinear data structures. Finally, to further boost model noise resilience, computational efficiency, and interpretability, while overcoming the NP-hardness of L-0-norm solutions and the non-smoothness of L-1-norm, we incorporate a computable L-2,(p)-norm (0

关键词： Dual-graph regularized L-2,L-p-norm regularization terms Stable adaptive spectral clustering non-negative matrix factorization non-negative matrix factorization

A novel study of kernel graph regularized semi-non-negative matrix factorization with orthogonal subspace for clustering

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Big Data Research 2025年 40卷

作者： Chen, Yasong Li, Wen Zhao, Junjian School of Mathematical Sciences TianGong University Tianjin300387 China

As a nonlinear extension of non-negative matrix factorization (NMF), Kernel non-negative matrix factorization (KNMF) has demonstrated greater effectiveness in revealing latent features from raw data. Building on this, this paper introduces kernel theory and effectively combines the advantages of semi-nonnegative constraints, graph regularization, and orthogonal subspace constraints to propose a novel model-Kernel Graph Regularized Semi-negative matrix factorization with Orthogonal Subspaces and Auxiliary Variables (semi-KGNMFOSV). This model introduces auxiliary variables and reformulates the optimization problem, successfully overcoming the convergence proof challenges typically associated with orthogonal subspace-constrained methods. Furthermore, the model utilizes kernel methods to effectively capture complex nonlinear structures in the data. The semi-nonnegative constraint, along with orthogonal subspace constraints incorporating auxiliary variables, enhances optimization efficiency, while graph regularization preserves the local geometric structure of the data. We develop an efficient optimization algorithm to solve the proposed model and conduct extensive experiments on multiple real-world datasets. Additionally, we investigate the impact of three different initialization strategies on the performance of the proposed algorithm. Experimental results demonstrate that, compared to classical and state-of-the-art methods, the proposed model exhibits superior performance across all three initialization strategies. © 2025 Elsevier Inc.

关键词： non-negative matrix factorization

Recommender Systems based on non-negative matrix factorization: A Survey

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IEEE Transactions on Artificial Intelligence

IEEE Transactions on Artificial Intelligence 2025年

作者： Ahmadian, Sajad Berahmand, Kamal Rostami, Mehrdad Forouzandeh, Saman Moradi, Parham Jalili, Mahdi Kermanshah University of Technology Faculty of Information Technology Kermanshah Iran School of Computer Science Brisbane Australia Faculty of Science Brisbane Australia Oulu Finland University of Oulu Faculty of Information Technology Oulu Finland University of Oulu Electrical Engineering Oulu Finland University of New South Wales School of Mathematics and Statistics SydneyNSW Australia RMIT University School of Engineering Melbourne Australia

Recommender systems have gained significant attention for their ability to model user preferences and predict future trends. Collaborative filtering, particularly through non-negative matrix factorization (NMF), is a popular method for building these systems. This paper presents a comprehensive survey of NMF-based methods in recommender systems, exploring enhancements that leverage key features such as sparsity, implicit feedback, and contextual information. We categorize developments into two main directions: pure NMF variants (including constrained, structured, and generalized NMF) and integrated NMF approaches (combining NMF with traditional and deep learning models). Our survey provides researchers and practitioners with a structured overview of the field's progress, identifies current challenges, and highlights promising directions for future research in NMF-based recommender systems. © 2020 IEEE.

关键词： non-negative matrix factorization

BAYESIAN PROBIT MULTI-STUDY non-negative matrix factorization FOR MUTATIONAL SIGNATURES

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arXiv 2025年

作者： Hansen, Blake Grabski, Isabella N. Parmigiani, Giovanni De Vito, Roberta Department of Biostatistics Brown University United States New York Genome Center United States Department of Data Science Dana Farber Cancer Institute Department of Biostatistics Harvard T.H. Chan School of Public Health United States Data Science Institute Brown University United States

Mutational signatures are patterns of somatic mutations in tumor genomes that provide insights into underlying mutagenic processes and cancer origin. Developing reliable methods for their estimation is of growing importance in cancer biology. Somatic mutation data are often collected for different cancer types, highlighting the need for multi-study approaches that enable joint analysis in a principled and integrative manner. Despite significant advancements, statistical models tailored for analyzing the genomes of multiple cancer types remain underexplored. In this work, we introduce a Bayesian Multi-Study non-negative matrix factorization (NMF) approach that uses mixture modeling to incorporate sparsity in the exposure weights of each subject to mutational signatures, allowing for individual tumor profiles to be represented by a subset rather than all signatures, and making this subset depend on covariates. This allows for a) more precise ability to identify meaningful contributions of mutational signatures at the individual level;b) estimation of the prevalence of activity of signatures within a cancer type, defined by the proportion of tumor profiles where a certain signature is present;and c) de-novo identification of interpretable patient subtypes based on the mutational signatures present within their mutational profile. We apply our approach to the mutational profiles of tumors from seven different cancer types, demonstrating its ability to accurately estimate mutational signatures while uncovering both individual and tissue-specific differences. An R package implementing our method is available at ***/blhansen/BAPmultiNMF. © 2025, CC BY.

关键词： non-negative matrix factorization

Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical non-negative matrix factorization

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arXiv 2025年

作者： Barron, Ryan C. Eren, Maksim E. Serafimova, Olga M. Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los AlamosNM United States CSEE UMBC BaltimoreMD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los AlamosNM United States Holland & Hart LLP Santa FeNM United States

Agentic Generative AI, powered by Large Language Models (LLMs) and enhanced with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformative technology applicable across specialized domains such as legal systems, research, recommender systems, cybersecurity, and global security, including proliferation research. This technology excels at inferring relationships within vast unstructured or semi-structured datasets. The legal domain we focus on here comprises inherently complex data characterized by extensive, interrelated, and semi-structured knowledge systems with complex relations. It comprises constitutions, statutes, regulations, and case law. Extracting insights and navigating the intricate networks of legal documents and their relations is crucial for effective legal research and decision-making. Here, we introduce a generative AI system that integrates RAG, VS, and KG, constructed via non-negative matrix factorization (NMF), to enhance legal information retrieval and AI reasoning and minimize hallucinations. In the legal system, these technologies empower AI agents to identify and analyze complex connections among cases, statutes, and legal precedents, uncovering hidden relationships and predicting legal trends—challenging tasks that are essential for ensuring justice and improving operational efficiency. Our system employs web scraping techniques to systematically collect legal texts, such as statutes, constitutional provisions, and case law, from publicly accessible platforms like Justia. It bridges the gap between traditional keyword-based searches and contextual understanding by leveraging advanced semantic representations, hierarchical relationships, and latent topic discovery. This approach is demonstrated in legal document clustering, summarization, and cross-referencing tasks. The framework marks a significant step toward augmenting legal research with scalable, interpretable, and accurate retrieva

关键词： non-negative matrix factorization

Applying non-negative matrix factorization with covariates to multivariate time series data as a vector autoregression model

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arXiv 2025年

作者： Satoh, Kenichi Faculty of Data Science Shiga University Banba 1-1-1 Shiga Hikone522-8522 Japan

non-negative matrix factorization (NMF) is a powerful dimensionality reduction technique that has been applied to time series data in recent years. This paper proposes a novel framework that integrates NMF with a vector autoregression (VAR) model to capture both latent structure and temporal dependencies in multivariate time series (MTS) data. The proposed model formaly includes ordinal autoregression in special cases, which is a natural extension while still accommodating high-dimensional data. By representing the NMF coefficient matrix as a VAR model, the framework leverages the interpretability of NMF while incorporating the dynamic characteristics of time series data. This approach allows for the extraction of meaningful features and accurate predictions in time series data. Copyright © 2025, The Authors. All rights reserved.

关键词： non-negative matrix factorization

Including local feature interactions in deep non-negative matrix factorization networks improves performance

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arXiv 2025年

作者： Nouri, Mahbod Rotermund, David Garcia-Ortiz, Alberto Pawelzik, Klaus R. University of Bremen Institute for Theoretical Physics Bremen Bremen28359 Germany Bremen Bremen28359 Germany

The brain uses positive signals as a means of signaling. Forward interactions in the early visual cortex are also positive, realized by excitatory synapses. Only local interactions also include inhibition. non-negative matrix factorization (NMF) captures the biological constraint of positive long-range interactions and can be implemented with stochastic spikes. While NMF can serve as an abstract formalization of early neural processing in the visual system, the performance of deep convolutional networks with NMF modules does not match that of CNNs of similar size. However, when the local NMF modules are each followed by a module that mixes the NMF’s positive activities, the performances on the benchmark data exceed that of vanilla deep convolutional networks of similar size. This setting can be considered a biologically more plausible emulation of the processing in cortical (hyper-)columns with the potential to improve the performance of deep networks. © 2025, CC BY-NC-ND.

关键词： non-negative matrix factorization