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Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation 认领 引用
1
作者 Xin Luo Fanghui Bi Tiantian He 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1642-1656,共15页
Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent ap... Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent approaches never consider higher-order spatiotemporal connectivity within Qo S data,thus suffering from inferior performance.To address this critical issue,this paper presents spatiotemporal graph convolutional network(GCN)that is equipped with the functionality of latent factorization of tensors(SGLFT).It is achieved by introducing three key innovations:1)Proposing a tensor graph convolution based on the generalized tensor product technique for uniformly modeling the temporal and spatial patterns within dynamic user-service graphs;2)Incorporating the built layer-wise graph convolution into tensor factorization for efficiently capturing the implied spatiotemporal high-order connectivity;and 3)Developing a nodelevel attention pooling mechanism to perceive feature differences among neighbors and across time slots.Theoretical derivations are conducted to demonstrate that the expressivity of the graph neural network proposed in this paper is evidently higher than that of vanilla GCNs.Empirical studies on eight large-scale testing cases arising from two real-world dynamic Qo S datasets show that SGLFT substantially outperforms state-of-the-art Qo S estimators regarding estimation accuracy for missing dynamic QoS data. 展开更多
关键词 Cloud service data science dynamic quality-of-service estimation graph convolutional networks(GCNs) latent factorization of tensors latent feature analysis non-euclidean data representation learning tensor product
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Privacy-Preserving Parallel Non-Negative Matrix Factorization with Edge Computing 认领 引用
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作者 Wenxuan Yu Wenjing Gao +2 位作者 Jiuru Wang Rong Hao Jia Yu 《Computers, Materials & Continua》 SCIE EI 2026年第6期498-518,共21页
Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy compu... Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers,effectively solving the problem of local computing difficulties.However,the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation,resulting in low efficiency on the server side.In order to improve the efficiency of outsourcing computation,we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers.We adopt the matrix blocking technique to divide the computation task into multiple subtasks,and design the NMF parallel computation algorithm based on the multiplication updating rule.The proposed scheme implements the parallel outsourcing of non-negative matrix factorization based on multiple edge servers.We use random permutation matrices to encrypt original matrix,thereby protecting data privacy.In addition,we utilize the iterative nature of the NMF algorithm for result verification.Theoretical analysis and experimental results prove the advantages of the proposed scheme. 展开更多
关键词 Secure outsourcing computation non-negative matrix factorization parallel outsourcing edge computing
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A Spatial-Temporal Traffic Prediction Algorithm Based on Non-Negative Tensor Factorization 认领 引用
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作者 Xiaoxiong Yang Dingde Jiang +1 位作者 Yi Zhang Zhihan Lyu 《Computers, Materials & Continua》 SCIE EI 2026年第8期2432-2445,共14页
The advancement of communication technology has made traffic engineering a critical issue in network systems.The traffic matrix is essential data that supports traffic engineering.The functionality of routing planning... The advancement of communication technology has made traffic engineering a critical issue in network systems.The traffic matrix is essential data that supports traffic engineering.The functionality of routing planning,network monitoring,and other modules within intelligent network management systems relies heavily on the network traffic matrix.However,real-time measurement of the network traffic matrix is costly and often suffers from missing or anomalous values.Consequently,long-term network traffic prediction presents significant challenges.Existing methods often fail to comprehensively address the multidimensional characteristics of traffic and the computational costs of the algorithms.To address these issues,we propose an efficient traffic prediction algorithm based on tensor factorization.First,we introduce a non-negative tensor factorization algorithm that accounts for link errors.This algorithm captures the spatial-temporal characteristics of traffic from different modes,thereby enhancing prediction efficiency.Next,we integrate the tensor factor matrix with a seasonal differential autoregressive moving average model in the temporal mode to identify traffic trends and complete the traffic prediction.Experimental results based on real data demonstrate that our algorithm performs exceptionally well in multi-step predictions and in capturing abnormal fluctuations. 展开更多
关键词 Flow tensor factorization multidimensional features traffic prediction
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Neural Tucker Factorization 认领 引用 被引量：1
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作者 Peng Tang Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期475-477,共3页
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-... Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework. 展开更多
关键词 neu tuc f neural tucker factorization latent factorization model high dimensional tensor tucker decomposition framework neural network incomplete tensor latent factorization
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Multi-Aspect Self-Attending Neural Tucker Factorization for Spatiotemporal Representation Learning 认领 引用
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作者 Yikai Hou Peng Tang Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期986-988,共3页
Dear Editor,This letter introduces a neural network-based tensor factorization approach tailored for learning spatiotemporal representations of highdimensional and incomplete(HDI)tensors,namely the multi-aspect self-a... Dear Editor,This letter introduces a neural network-based tensor factorization approach tailored for learning spatiotemporal representations of highdimensional and incomplete(HDI)tensors,namely the multi-aspect self-attending neural Tucker factorization(MASANT).The model is elaborately designed for modeling intricate nonlinear spatiotemporal feature interaction patterns hidden in real world data with a two-fold idea. 展开更多
关键词 incomplete tensor nonlinear interaction real world data learning spatiotemporal representations high dimensional tensor tensor factorization modeling intricate nonlinear spatiotemporal feature interaction patterns neural network
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Lightweight and Explainable Anomaly Detection in CAN Bus Traffic via Non-Negative Matrix Factorization 认领 引用
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作者 Anandkumar Balasubramaniam Seung Yeob Nam 《Computers, Materials & Continua》 SCIE EI 2026年第6期1263-1306,共44页
The increasing connectivity of modern vehicles exposes the in-vehicle controller area network(CAN)bus to various cyberattacks,including denial-of-service,fuzzy injection,and spoofing attacks.Existing machine learning ... The increasing connectivity of modern vehicles exposes the in-vehicle controller area network(CAN)bus to various cyberattacks,including denial-of-service,fuzzy injection,and spoofing attacks.Existing machine learning and deep learning intrusion detection systems(IDS)often rely on labeled data,struggle with class imbalance,lack interpretability,and fail to generalize well across different datasets.This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization(NMF)to address these limitations.Our contributions include:(i)evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor(NMF-W)for classical,unsupervised,and deep sequence models;(ii)providing comprehensive benchmarking on the car-hacking dataset(CHD),demonstrating improved robustness in mixed-attack and cross-attack scenarios,with class imbalance addressed through oversampling and class weighting;(iii)offering a component-level interpretability analysis that links NMF factors to meaningful CAN traffic patterns;and(iv)validating cross-dataset transferability on the offset-ratio and time interval-based intrusion detection system(OTIDS)dataset.Additional ablation and efficiency studies confirm the practical feasibility of deploying NMF-based IDS on embedded automotive controllers.Overall,this work presents a balanced IDS solution that combines detection accuracy,computational efficiency,and explainability,thereby advancing the security of in-vehicle networks. 展开更多
关键词 Anomaly detection CAN bus explainable AI intrusion detection system(IDS) non-negative matrix factorization(NMF)
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Robust azimuth ambiguity detection method in SAR images based on non-negative matrix factorization 认领 引用
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作者 LI Jieshuang TAO Mingliang +2 位作者 CUI Lei LIU Yanyang WANG Ling 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期743-754,共12页
Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and... Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks.To overcome these specific limitations,this paper proposes an improved detection method.It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities.Non-negative matrix factorization is then employed to decompose this matrix,effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds.Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods. 展开更多
关键词 synthetic aperture radar(SAR) azimuth ambiguity multi-look processing nonnegative matrix factorization(NMF)
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Unconstrained Latent Factorization-Based Improved Relief-F 认领 引用
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作者 Wenting Chen Ming Li 《Journal of Electronic Research and Application》 2026年第4期15-21,共7页
Feature selection is essential for dimensionality reduction on big data,but it faces considerable challenges when applied to high-dimensional and sparse datasets.To address these challenges,this paper proposes Unconst... Feature selection is essential for dimensionality reduction on big data,but it faces considerable challenges when applied to high-dimensional and sparse datasets.To address these challenges,this paper proposes Unconstrained Latent Factorization-based Improved Relief-F(ULF-IR),a novel feature selection method tailored for such complex scenarios.The method integrates two main components:(1)a double factorization(DF)-based unconstrained latent factor model is employed to accurately reconstruct missing data without relying on pre-imputation or strict non-negativity constraints;(2)an improved Relief-F(IRelief-F)algorithm assigns reliable importance weights to features,effectively differentiating among highly similar features even in the presence of noise introduced during imputation.Comprehensive experiments on three real-world datasets show that ULF-IR consistently surpasses state-of-the-art methods in both classification accuracy and robustness,demonstrating its effectiveness as a dependable solution for feature selection on high-dimensional,incomplete data. 展开更多
关键词 Feature selection Incomplete data Latent factor Relief-F
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Predicting CircRNA-Disease Associations via Non-Negative Matrix Factorization Fused with Multiple Similarity Networks 认领 引用 被引量：1
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作者 LU Pengli LI Shiying 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第4期709-719,共11页
CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs a... CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases. 展开更多
关键词 circRNA-disease associations circRNA sequence data disease semantic information non-negative matrix factorization
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Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction 认领 引用
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作者 Minzhi Chen Li Tao +1 位作者 Jungang Lou Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第3期633-635,共3页
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and... Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T). 展开更多
关键词 health management battery pack bp can latent factorization tensors battery cycle life prediction health management phm battery cycle battery pack battery management system bms which
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Changes in factor profiles deriving from photochemical losses of volatile organic compounds:Insight from daytime and nighttime positive matrix factorization ana 认领 引用 被引量：1
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作者 Baoshuang Liu Tao Yang +9 位作者 Sicong Kang Fuquan Wang Haixu Zhang Man Xu Wei Wang Jinrui Bai Shaojie Song Qili Dai Yinchang Feng Philip K.Hopke 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第5期627-639,共13页
Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data ... Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty. 展开更多
关键词 Volatile organic compounds Dispersion normalization Photochemical loss Factor profile Positive matrix factorization
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Source apportionment of PM2.5 using dispersion normalized positive matrix factorization(DN-PMF)in Beijing and Baoding,China 认领 引用 被引量：1
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作者 Ilhan Ryoo Taeyeon Kim +6 位作者 Jiwon Ryu Yeonseung Cheong Kwang-joo Moon Kwon-ho Jeon Philip K.Hopke Seung-Muk Yi Jieun Park 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第9期395-408,共14页
Fine particulatematter(PM2.5)samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM2.5 in which the average mass concentration exceeded 75μg/m3 were freque... Fine particulatematter(PM2.5)samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM2.5 in which the average mass concentration exceeded 75μg/m3 were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM2.5 as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM2.5 sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities. 展开更多
关键词 Source apportionment Dispersion normalized positive matrix factorization Adjacent cities Inter-city impact Source location Heating season Air quality management
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A Comprehensive Review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization 认领 引用
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作者 Qicong Hu Hao Wu Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2399-2426,共28页
High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a c... High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a commercial recommender system or the user-user interactions in a social network services system. The factorization of such an HDI matrix can embed the involved entities into the low-dimensional feature space for acquiring their principal representation, which is a vital task in various application scenes and is often established through the Latent Factor Analysis(LFA). Nevertheless, an HDI matrix can be huge when the corresponding application explodes to involve millions of users, items, or other interactive nodes. In this case, a parallel optimization algorithm is desired for raising the scalability and time efficiency of an LFA model. This paper provides a comprehensive review of the existing parallel optimization algorithms for the LFA model. Specifically, it performs: 1) discussion and summary of these algorithms based on computing architecture and mode, 2) empirical studies of representative models, and3) summary of the current challenges and future directions in this domain. This survey aims to offer an exhaustive review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization, thereby fostering further research in this field. 展开更多
关键词 Data-related application high-dimensional and incomplete(HDI)data high-performance computing latent feature analysis(LFA) matrix factorization parallel optimization algorithm
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Nonlinear Control for Unstable Networked Plants in the Presence of Actuator and Sensor Limitations Using Robust Right Coprime Factorization 认领 引用
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作者 Yuanhong Xu Mingcong Deng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第3期516-527,共12页
In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and l... In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise.With this nonlinear control method,we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant,so that bounded input bounded output(BIBO)stability can be guaranteed.Next,continuous-time generalized predictive control(CGPC)is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics.Meanwhile,a second-Do F(degrees of freedom)switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed.By using the CGPC controller that possesses predictive behavior and the second-Do F switched stabilizer,the overall stability of the plant subjected to actuator limitations is guaranteed.To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation,we implement an identity operator definition in the feedback loop to compensate for these adverse effects.Further,a pre-operator is designed to ensure that the plant output tracks the reference input.Finally,the effectiveness of the proposed design scheme is demonstrated by simulations. 展开更多
关键词 Actuator and sensor limitations identity operator definition network-induced limitations robust right coprime factorization unstable plant
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Research on Library Data Governance for Data Factorization 认领 引用
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作者 Yan Jiang 《Journal of Electronic Research and Application》 2025年第6期159-166,共8页
Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationa... Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally,this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization.The aim is to facilitate the intelligent transformation and high-quality development of libraries. 展开更多
关键词 Data factorization Libraries Data governance Mechanism of action Practical paths
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[0, ki ]1m -FACTORIZATIONS ORTHOGONAL TO A SUBGRAPH 认领 引用
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作者 马润年 许进 高行山 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2001年第5期593-596,共4页
Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m... Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m}, is a [0, k(i)](1)(m) -factorization of G. If H is a subgraph with m edges in graph G and / E (H) boolean AND E(F-i) / = 1 for all 1 less than or equal to i less than or equal to m, then we can call that (F) over bar is orthogonal to H. It is proved that if G is a [0, k(1) + ... + k(m) - m + 1]-graph, H is a subgraph with m edges in G, then graph G has a [0, k(i)](1)(m)-factorization orthogonal to H. 展开更多
关键词 graph factor factorization orthogonal factorization
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A Finite Method for Computing the Drazin and Core-EP Inverses of Matrices Based on Partial Full-Rank Factorization 认领 引用
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作者 Xuzhou Chen Jun Ji 《Communications in Mathematical Research》 CSCD 2021年第4期448-461,共14页
This paper presents a formula for the Drazin inverses of matrices based on a sequence of partial full-rank factorizations which theoretically extends the classic full-rank factorization method for computing the Drazin... This paper presents a formula for the Drazin inverses of matrices based on a sequence of partial full-rank factorizations which theoretically extends the classic full-rank factorization method for computing the Drazin inverses established by R.E.Cline.The result is then extended to the core-EP inverses. 展开更多
关键词 Core-EP inverse Drazin inverse finite method full-rank factorization partial full-rank factorization
Some Results on the Problem of Updating the Hyperbolic Matrix Factorizations 认领 引用
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作者 Hanyu LI Hu YANG 《Journal of Mathematical Research with Applications》 CSCD 2013年第1期35-44,共10页
This paper considers the updating problem of the hyperbolic matrix factorizations. The sufficient conditions for the existence of the updated hyperbolic matrix factorizations are first provided. Then, some differentia... This paper considers the updating problem of the hyperbolic matrix factorizations. The sufficient conditions for the existence of the updated hyperbolic matrix factorizations are first provided. Then, some differential inequalities and first order perturbation expansions for the updated hyperbolic factors are derived. These results generalize the corresponding ones for the updating problem of the classical QR factorization obtained by Jiguang SUN. 展开更多
关键词 hyperbolic matrix factorization hyperbolic QR factorization hyperbolic polarfactorization updating problem perturbation analysis.
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Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization 认领 引用 被引量：39
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作者 GAO Huizhong LIANG Lin +1 位作者 CHEN Xiaoguang XU Guanghua 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第1期96-105,共10页
Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smar... Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space. 展开更多
关键词 time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction
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Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation 认领 引用 被引量：12
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作者 Chengcai Leng Hai Zhang +2 位作者 Guorong Cai Irene Cheng Anup Basu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期584-595,共12页
This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information ... This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods. 展开更多
关键词 Data clustering dimensionality reduction graph regularization Lp smooth non-negative matrix factorization(SNMF)
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