1. 广东工业大学 数学与统计学院,广东,广州,510520
2. 南京大学 新型软件技术国家重点实验室,江苏,南京,210093
樊娟(1997–),女,硕士研究生,主要研究方向为图像处理、数据挖掘
邓秀勤(1966–),女,教授,硕士生导师,主要研究方向为数据挖掘、机器学习,E-mail:dxq706@gdut.edu.cn
纸质出版:2023
移动端阅览
樊娟, 邓秀勤, 刘玉兰. 一种基于Fréchet距离的谱聚类算法[J]. 广东工业大学学报, 2023, 40(2): 39-44.
Fan Juan, Deng Xiu-qin, Liu Yu-lan. A Spectral Clustering Algorithm Based on Fréchet Distance[J]. 2023, 40(2): 39-44.
樊娟, 邓秀勤, 刘玉兰. 一种基于Fréchet距离的谱聚类算法[J]. 广东工业大学学报, 2023, 40(2): 39-44. DOI: 10.12052/gdutxb.210191.
Fan Juan, Deng Xiu-qin, Liu Yu-lan. A Spectral Clustering Algorithm Based on Fréchet Distance[J]. 2023, 40(2): 39-44. DOI: 10.12052/gdutxb.210191.
为提升谱聚类的聚类精度和适用性,提出了一种基于Fréchet距离的谱聚类算法(A Spectral Clustering Algorithm Based on Fréchet Distance
FSC),通过Fréchet距离构建相似度矩阵,并将重构的相似矩阵应用于谱聚类中。利用Fréchet距离度量数据特征维度的相似性对样本的多个特征进行分析,进而扩展典型谱聚类算法的适用性。FSC不仅适用于低维流形结构清晰的数据,也适用于高维或稀疏数据,如高光谱图像数据。在3个经典的高光谱图像上的实验结果表明,FSC算法有效提高了高光谱图像聚类的精度。
In order to improve the clustering accuracy and applicability of spectral clustering
a spectral clustering algorithm based on Fréchet distance (called FSC) is proposed. Firstly a similarity matrix is constructed by Fréchet distance
then the reconstructed similarity matrix is applied to the spectral clustering. Using Fréchet distance to measure the similarity of data feature dimensions can extend the applicability of typical spectral clustering algorithms. FSC is not only suitable for data with clear low-dimensional manifold structure
but also for high-dimensional or sparse data
such as hyperspectral images. The experimental results on three hyperspectral images show that the FSC algorithm effectively improves the accuracy of hyperspectral images clustering.
ZHU M, HUANG D, HU X J, et al . Application of hyperspectral technology in detection of agricultural products and food: a review [J ] . Food Science Nutrition, 2020, 8(10): 5206-5214.
MA S P, LIANG L, TENG S H. A lightweight hyperspectral remote sensing image classification method [J]. Journal of Guangdong University of Technology, 2021, 38(3): 29-35.
ALSAMHI S H, MA O. Collaboration of drone and internet of public safety things in smart cities: an overview of QoS and network performance optimization [J]. Drones, 2019, 3(1): 13.
KUMAR V S, SIVAPRAKASAM S A, NAGANATHAN R, et al . Fast K-means technique for hyper-spectral image segmentation by multiband reduction [J ] . Pollack Periodica, 2019, 14(3): 201-212.
SALEM R B, ETTABAA K S, HAMDI M A. Fuzzy C-mean for unsupervised spectral-spatial SVM classification of hyperspectral images[C]//2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). Piscataway, NJ: IEEE, 2017: 759-765.
SHAHID K T, MALHOTRA A, SCHIZAS I D, et al . Unsupervised kernel correlations based hyperspectral clustering with missing pixels [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(6): 1799-1810.
WANG R, NIE F, YU W. Fast spectral clustering with anchor graph for large hyperspectral images [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11): 2003-2007.
YE J, ZHAO Z, LIU H. Adaptive distance metric learning for clustering[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2007: 1-7.
SUN W, PENG J, YANG G, et al . Correntropy-based sparse spectral clustering for hyperspectral band selection [J ] . IEEE Geoscience and Remote Sensing Letters, 2019, 17(3): 484-488.
ZHAO Y, YUAN Y, WANG Q. Fast spectral clustering for unsupervised hyperspectral image classification [J]. Remote Sensing, 2019, 11(4): 399.
ZHAI H, ZHANG H, ZHANG L, et al . A new sparse subspace clustering algorithm for hyperspectral remote sensing imagery [J ] . IEEE Geoscience and Remote Sensing Letters, 2016, 14(1): 43-47.
LONG Y H, DENG X Q, WANG Z W. Weighted block sparse subspace clustering algorithm based on information entropy [J]. Journal of Data Acquisition and Processing, 2021, 36(3): 544-555.
YAN Q, DING Y, ZHANG J J, et al . A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery [J ] . Cognitive Systems Research, 2019, 53: 98-110.
WENG H, WANG S, WAN Y, et al . Discrete Fréchet distance algorithm based criterion of transformer differential protection with the immunity to saturation of current transformer [J ] . International Journal of Electrical Power Energy Systems, 2020, 115: 105449.
BANG Y, KIM J, YU K. An improved map-matching technique based on the Fréchet distance approach for pedestrian navigation services [J]. Sensors, 2016, 16(10): 1768.
FRÉCHET M M. Sur quelques points du calcul fonctionnel [J]. Rendiconti del Circolo Matematico di Palermo (1884-1940), 1906, 22(1): 1-72.
ZHANG W H. Embedded module of curve similarity judging based on Fréchet distance [J]. Microcontrollers Embedded Systems, 2020, 20(9): 17-20.
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621