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Target Controllability of Multi-Layer Networks With High-Dimensional Nodes
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作者 Lifu Wang Zhaofei Li +1 位作者 Ge Guo Zhi Kong 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第9期1999-2010,共12页
This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighte... This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion. 展开更多
关键词 high-dimensional nodes inter-layer couplings multi-layer networks target controllability
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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition
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作者 Liya Yue Pei Hu +1 位作者 Shu-Chuan Chu Jeng-Shyang Pan 《Computers, Materials & Continua》 SCIE EI 2024年第2期1957-1975,共19页
Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is ext... Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER. 展开更多
关键词 Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer MULTI-OBJECTIVE
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An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method
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作者 Xiaoyi Wang Xinyue Chang +2 位作者 Wenxuan Wang Zijie Qiao Feng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1775-1796,共22页
The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the effi... The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method. 展开更多
关键词 Reliability-based design optimization high-dimensional model decomposition point estimation method Lagrange interpolation aviation hydraulic piping system
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Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data
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作者 LIU Wei LI Yingqiu 《应用概率统计》 CSCD 北大核心 2024年第5期783-799,共17页
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef... In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated. 展开更多
关键词 high-dimensional survival data censored composite conditional quantile coefficient sure screening property rank consistency property
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Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data
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作者 Meiyin Wang Wanzhou Ye 《Advances in Pure Mathematics》 2024年第4期214-227,共14页
The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based o... The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method. 展开更多
关键词 high-dimensional Covariance Matrix Missing Data Sub-Gaussian Noise Optimal Estimation
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Observation points classifier ensemble for high-dimensional imbalanced classification 被引量:1
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作者 Yulin He Xu Li +3 位作者 Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第2期500-517,共18页
In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)... In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems. 展开更多
关键词 classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism
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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection
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作者 Yanlu Gong Junhai Zhou +2 位作者 Quanwang Wu MengChu Zhou Junhao Wen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第9期1834-1844,共11页
As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected featu... As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms. 展开更多
关键词 Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive
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K-Hyperparameter Tuning in High-Dimensional Space Clustering:Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes
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作者 Rufus Gikera Jonathan Mwaura +1 位作者 Elizaphan Muuro Shadrack Mambo 《Journal on Artificial Intelligence》 2023年第1期75-112,共38页
k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets.However,one of its setbacks is the challenge of identifying the correct k-hyperparameter value.Tuning this v... k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets.However,one of its setbacks is the challenge of identifying the correct k-hyperparameter value.Tuning this value correctly is critical for building effective k-means models.The use of the traditional elbow method to help identify this value has a long-standing literature.However,when using this method with certain datasets,smooth curves may appear,making it challenging to identify the k-value due to its unclear nature.On the other hand,various internal validation indexes,which are proposed as a solution to this issue,may be inconsistent.Although various techniques for solving smooth elbow challenges exist,k-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue.In this paper,we have first reviewed the existing techniques for solving smooth elbow challenges.The identified research gaps are then utilized in the development of the new technique.The new technique,referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes,is then validated in high-dimensional space clustering.The optimal k-value,tuned by this technique using a voting scheme,is a trade-off between the number of clusters visualized in the autoencoder’s latent space,k-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivative f″′(k)(1+f′(k)^(2))−3 f″(k)^(2)f″((k)2f′(k),at the elbow.Experimental results based on the Cochran’s Q test,ANOVA,and McNemar’s score indicate a relatively good performance of the newly developed technique in k-hyperparameter tuning. 展开更多
关键词 k-hyperparameter tuning high-dimensional smooth elbow
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Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder
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作者 S.Abinaya K.Uttej Kumar 《Computers, Materials & Continua》 SCIE EI 2024年第2期2269-2286,共18页
A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer prefe... A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures. 展开更多
关键词 Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering
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Guaranteed Cost Consensus for High-dimensional Multi-agent Systems With Time-varying Delays 被引量:8
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作者 Zhong Wang Ming He +2 位作者 Tang Zheng Zhiliang Fan Guangbin Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第1期181-189,共9页
Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for... Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for high-dimensiona multi-agent systems with time-varying delays, where a cos function is defined based on state errors among neighboring agents and control inputs of all the agents. By the state space decomposition approach and the linear matrix inequality(LMI)sufficient conditions for guaranteed cost consensus and consensu alization are given. Moreover, a guaranteed cost upper bound o the cost function is determined. It should be mentioned that these LMI criteria are dependent on the change rate of time delays and the maximum time delay, the guaranteed cost upper bound is only dependent on the maximum time delay but independen of the Laplacian matrix. Finally, numerical simulations are given to demonstrate theoretical results. 展开更多
关键词 Guaranteed cost consensus high-dimensional multi-agent system time-varying delay
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Chip-Based High-Dimensional Optical Neural Network 被引量:6
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作者 Xinyu Wang Peng Xie +1 位作者 Bohan Chen Xingcai Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2022年第12期570-578,共9页
Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high paralleliz... Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing. 展开更多
关键词 Integrated optics Optical neural network high-dimension Mach-Zehnder interferometer Nonlinear activation function Parallel high-capacity analog computing
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A Sequence Image Matching Method Based on Improved High-Dimensional Combined Features 被引量:2
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作者 Leng Xuefei Gong Zhe +1 位作者 Fu Runzhe Liu Yang 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2018年第5期820-828,共9页
Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dim... Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes. 展开更多
关键词 SEQUENCE image MATCHING navigation DELAUNAY TRIANGULATION high-dimensional combined feature k-nearest NEIGHBOR
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Perfect Transfer of Many-Particle Quantum State via High-Dimensional Systems with Spectrum-Matched Symmetry 被引量:3
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作者 LI Ying SONG Zhi SUN Chang-Pu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2007年第3X期445-448,共4页
The quantum state transmission through the medium of high-dimensional many-particle system (boson or spinless fermion) is generally studied with a symmetry analysis. We discover that, if the spectrum of a Hamiltonia... The quantum state transmission through the medium of high-dimensional many-particle system (boson or spinless fermion) is generally studied with a symmetry analysis. We discover that, if the spectrum of a Hamiltonian matches the symmetry of a fermion or boson system in a certain fashion, a perfect quantum state transfer can be implemented without any operation on the medium with pre-engineered nearest neighbor (NN). We also study a simple but realistic near half-filled tight-bindlng fermion system wlth uniform NN hopping integral. We show that an arbitrary many-particle state near the fermi surface can be perfectly transferred to its translational counterpart. 展开更多
关键词 high-dimensional many-particle system perfect quantum state transfer
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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications 被引量:13
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作者 Mingsheng Shang Xin Luo +3 位作者 Zhigang Liu Jia Chen Ye Yuan MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第1期131-141,共11页
Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts itera... Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models. 展开更多
关键词 Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning
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Similarity measurement method of high-dimensional data based on normalized net lattice subspace 被引量:4
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作者 李文法 Wang Gongming +1 位作者 Li Ke Huang Su 《High Technology Letters》 EI CAS 2017年第2期179-184,共6页
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities... The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction. 展开更多
关键词 high-dimensional data the curse of dimensionality SIMILARITY NORMALIZATION SUBSPACE NPsim
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Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data 被引量:5
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作者 Di Wu Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第4期796-805,共10页
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat... High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices. 展开更多
关键词 high-dimensional and sparse matrix L1-norm L2 norm latent factor model recommender system smooth L1-norm
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High-Dimensional Schwarzian Derivatives and Painlevé Integrable Models 被引量:1
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作者 ZHANG Shun-Li TANG Xiao-Yan LOU Sen-Yue 《Communications in Theoretical Physics》 SCIE CAS CSCD 2002年第11期513-516,共4页
Because all the known integrable models possess Schwarzian forms with Mobious transformation invariance,it may be one of the best ways to find new integrable models starting from some suitable Mobious transformation i... Because all the known integrable models possess Schwarzian forms with Mobious transformation invariance,it may be one of the best ways to find new integrable models starting from some suitable Mobious transformation invariant equations. In this paper, we study the Painlevé integrability of some special (3+1)-dimensional Schwarzian models. 展开更多
关键词 Mobious invariance Schwarzian derivatives high-dimensional INTEGRABLE MODELS
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Computation of the Rational Representation for Solutions of High-dimensional Systems 被引量:3
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作者 TAN CHANG ZHANG SHU-GONG 《Communications in Mathematical Research》 CSCD 2010年第2期119-130,共12页
This paper deals with the representation of the solutions of a polynomial system, and concentrates on the high-dimensional case. Based on the rational univari- ate representation of zero-dimensional polynomial systems... This paper deals with the representation of the solutions of a polynomial system, and concentrates on the high-dimensional case. Based on the rational univari- ate representation of zero-dimensional polynomial systems, we give a new description called rational representation for the solutions of a high-dimensional polynomial sys- tem and propose an algorithm for computing it. By this way all the solutions of any high-dimensional polynomial system can be represented by a set of so-called rational- representation sets. 展开更多
关键词 rational univariate representation high-dimensional ideal maximally independent set rational representation irreducible component
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New semi-quantum key agreement protocol based on high-dimensional single-particle states 被引量:2
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作者 Huan-Huan Li Li-Hua Gong Nan-Run Zhou 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第11期189-196,共8页
A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states.Different from the previous semi-quantum key agreement protocols based on the two-level quantum sy... A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states.Different from the previous semi-quantum key agreement protocols based on the two-level quantum system,the propounded protocol makes use of the advantage of the high-dimensional quantum system,which possesses higher efficiency and better robustness against eavesdropping.Besides,the protocol allows the classical participant to encode the secret key with qudit shifting operations without involving any quantum measurement abilities.The designed semi-quantum key agreement protocol could resist both participant attacks and outsider attacks.Meanwhile,the conjoint analysis of security and efficiency provides an appropriate choice for reference on the dimension of single-particle states and the number of decoy states. 展开更多
关键词 semi-quantum key agreement protocol high-dimensional quantum state quantum cryptography quantum communication
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CSFW-SC: Cuckoo Search Fuzzy-Weighting Algorithm for Subspace Clustering Applying to High-Dimensional Clustering 被引量:1
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作者 WANG Jindong HE Jiajing +1 位作者 ZHANG Hengwei YU Zhiyong 《China Communications》 SCIE CSCD 2015年第S2期55-63,共9页
Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subsp... Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms. 展开更多
关键词 high-dimensional data CLUSTERING soft SUBSPACE CUCKOO SEARCH FUZZY CLUSTERING
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