A Spectral Clustering Algorithm Based on Fréchet Distance

Fan Juan; Deng Xiu-qin; Liu Yu-lan

doi:10.12052/gdutxb.210191

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A Spectral Clustering Algorithm Based on Fréchet Distance

更新时间：2026-05-27

- A Spectral Clustering Algorithm Based on Fréchet Distance
- Vol. 40, Issue 2, Pages: 39-44(2023)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210191
  CLC： TP751.1
- Published：2023
- 稿件说明：
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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.

摘要

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。

Abstract

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.

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references

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