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spectral clustering eigenvector selection of hyperspectral image based on the coincidence degree of data distribution

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International Journal of Digital Earth 2023年第1期16卷 3489-3512页

作者： Zhongliang Ren Qiuping Zhai Lin Sun College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoPeople’s Republic of China Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection School of Resource and Environmental SciencesLinyi UniversityPeople’s Republic of China

spectral clustering is a well-regarded subspace clustering algorithm that exhibits outstanding performance in hyperspectral image classification through eigenvalue decomposition of the Laplacian matrix.However,its classification accuracy is severely limited by the selected eigenvectors,and the commonly used eigenvectors not only fail to guarantee the inclusion of detailed discriminative information,but also have high computational complexity.To address these challenges,we proposed an intuitive eigenvector selection method based on the coincidence degree of data distribution(CDES).First,the clustering result of improved k-means,which can well reflect the spatial distribution of various types was used as the reference map.Then,the adjusted Rand index and adjusted mutual information were calculated to assess the data distribution consistency between each eigenvector and the reference map.Finally,the eigenvectors with high coincidence degrees were selected for clustering.A case study on hyperspectral mineral mapping demonstrated that the mapping accuracies of CDES are approximately 56.3%,15.5%,and 10.5%higher than those of the commonly used top,high entropy,and high relevance eigenvectors,and CDES can save more than 99%of the eigenvector selection time.Especially,due to the unsupervised nature of k-means,CDES provides a novel solution for autonomous feature selection of hyperspectral images.

关键词： Eigenvector selection spectral clustering coincidence degree of data distribution hyperspectral mineral mapping

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Quantum spectral clustering algorithm for unsupervised learning

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Science China(Information Sciences) 2022年第10期65卷 43-52页

作者： Qingyu LI Yuhan HUANG Shan JIN Xiaokai HOU Xiaoting WANG Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China

clustering is one of the most crucial problems in unsupervised learning, and the well-known k-means algorithm can be implemented on a quantum computer with a significant speedup. However, for the clustering problems that cannot be solved using the k-means algorithm, a powerful method called spectral clustering is used. In this study, we propose a circuit design to implement spectral clustering on a quantum processor with substantial speedup by initializing the processor into a maximally entangled state and encoding the data information into an efficiently simulatable Hamiltonian. Compared to the established quantum kmeans algorithms, our method does not require a quantum random access memory or a quantum adiabatic process. It relies on an appropriate embedding of quantum phase estimation into Grover’s search to gain the quantum speedup. Simulations demonstrate that our method effectively solves clustering problems and is an important supplement to quantum k-means algorithm for unsupervised learning.

关键词： quantum algorithm machine learning spectral clustering quantum phase estimation Grover's search Hamiltonian simulation

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Improved spectral clustering Clothing Image Segmentation Algorithm Based on Sparrow Search Algorithm

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Journal of Donghua University(English Edition) 2022年第4期39卷 340-344页

作者：黄文谙钱素琴 College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile&Fashion Technology Ministry of EducationDonghua UniversityShanghai 201620China

In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction.However,the traditional model requires high computing power and is easily affected by the initial center of clustering.It often falls into local optimization.Aiming at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this paper.The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity.In the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local optimization.In the end,the effectiveness of this algorithm is verified on clothing images in each environment.

关键词： clothing segmentation spectral clustering particle swarm optimization algorithm intelligent fashion design

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spectral clustering based on matrix perturbation theory

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Science in China(Series F) 2007年第1期50卷 63-81页

作者： TIAN Zheng LI XiaoBin JU YanWei Department of Applied Mathematics Northwestern Polytechnical University Xi'an 710072 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing 100080 China

This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenvectors. It shows that the number of clusters Is equal to the number of elgenvslues that are larger than 1, and the number of polnts In each of the clusters can be spproxlmsted by the associated elgenvslue. It also shows that the elgenvector of the weight rnatrlx can be used dlrectly to perform clusterlng; that Is, the dlrectlonsl angle between the two-row vectors of the mstrlx derlved from the elgenvectors Is s sultable distance measure for clustsrlng. As s result, an unsupervised spectral clusterlng slgorlthm based on welght mstrlx （USCAWM） Is developed. The experlmental results on s number of srtlficisl and real-world data sets show the correctness of the theoretical analysis.

关键词： spectral clustering weight matrix spectrum of weight matrix number of the clusters unsupervised spectral clustering algorithm based on weight matrix

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Bad Data Detection Algorithm for PMU Based on spectral clustering

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Journal of Modern Power Systems and Clean Energy 2020年第3期8卷 473-483页

作者： Zhiwei Yang Hao Liu Tianshu Bi Qixun Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

关键词： Phasor measurement units(PMUs) bad data detection event data identification decision tree spectral clustering

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Dual membership SVM method based on spectral clustering

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Journal of Systems Engineering and Electronics 2012年第2期23卷 225-232页

作者： Xiaodong Song Liyan Han School of Economics and Management Beihang UniversityBeijing 100191P.R.China

A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is pro- posed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of ＂overlapping＂ region between the two training classes. The proposed method handles sample ＂overlap＂ effi- ciently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.

关键词： dual membership model fuzzy support vector ma- chine （FSVM） spectral clustering sample ＂overlap＂.

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Unsupervised seismic facies analysis using sparse representation spectral clustering

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Applied Geophysics 2020年第4期17卷 533-543页

作者： Wang Yao-Jun Wang Liang-Ji Li Kun-Hong Liu Yu Luo Xian-Zhe Xing Kai School of Resource and Environment University of Electronic Science and Technology of ChinaChengdu 611731China Qiushi College Taiyuan University of TechnologyTaiyuan 030600China

Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.

关键词： seismic facies analysis spectral clustering sparse representation and unsupervised clustering

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Defense Against Poisoning Attack via Evaluating TrainingSamples Using Multiple spectral clustering Aggregation Method

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Computers, Materials & Continua 2019年第6期59卷 817-832页

作者： Wentao Zhao Pan Li Chengzhang Zhu Dan Liu Xiao Liu College of Computer National University of Defense TechnologyChangsha410073China Faculty of Engineering and Information Technology University of Technology Sydney2007Australia

The defense techniques for machine learning are critical yet challenging due tothe number and type of attacks for widely applied machine learning algorithms aresignificantly increasing. Among these attacks, the poisoning attack, which disturbsmachine learning algorithms by injecting poisoning samples, is an attack with the greatestthreat. In this paper, we focus on analyzing the characteristics of positioning samples andpropose a novel sample evaluation method to defend against the poisoning attack cateringfor the characteristics of poisoning samples. To capture the intrinsic data characteristicsfrom heterogeneous aspects, we first evaluate training data by multiple criteria, each ofwhich is reformulated from a spectral clustering. Then, we integrate the multipleevaluation scores generated by the multiple criteria through the proposed multiplespectral clustering aggregation (MSCA) method. Finally, we use the unified score as theindicator of poisoning attack samples. Experimental results on intrusion detection datasets show that MSCA significantly outperforms the K-means outlier detection in terms ofdata legality evaluation and poisoning attack detection.

关键词： Poisoning attack sample evaluation spectral clustering ensemble learning.

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Gravitational Inspired spectral clustering with Constraint

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Chinese Journal of Electronics 2015年第3期24卷 487-491页

作者： SUN Liping LUO Yonglong ZHENG Xiaoyao LV Jun College of National Territorial Resources and Tourism Anhui Normal University College of Mathematics and Computer Science Anhui Normal University

spectral clustering with pairwise constraints(i.e. mustlink and cannotlink) has been a hot topic in the machine learning community in recent years. Its performances are significantly influenced by utilizing the constraints. To make full use of the constraints’ effect, pairwise constraints are integrated into an affinity matrix based on the gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational inspired constrained spectral clustering(GCSC) algorithm is proposed in this paper. Our algorithm is evaluated on multiple benchmark classification datasets. Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.

关键词： spectral clustering Gravitation model Affinity matrix Pairwise constraint

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Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

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International Journal of Digital Earth 2021年第10期14卷 1452-1476页

作者： Yong Pang Weiwei Wang Liming Du Zhongjun Zhang Xiaojun Liang Yongning Li Zuyuan Wang Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijingPeople’s Republic of China Key Laboratory of Forestry Remote Sensing and Information System of National Forestry and Grassland Administration BeijingPeople’s Republic of China College of Artificial Intelligence Beijing Normal UniversityBeijingPeople’s Republic of China College of Forestry Hebei Agricultural UniversityBaodingPeople’s Republic of China Department of Land Change Science Swiss Federal Institute for ForestSnow and Landscape Research WSLBirmensdorfSwitzerland

The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.

关键词： Tree segmentation airborne LiDAR spectral clustering Nyström approximation sampling method

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