Structural α-Entropy Weighting Gaussian Mixture Model for Subspace Clustering

LI Kai; ZHANG Ke-xin

doi:10.12263/DZXB.20201146

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Structural α-Entropy Weighting Gaussian Mixture Model for Subspace Clustering

PAPERS | 更新时间：2025-12-11

- Structural α-Entropy Weighting Gaussian Mixture Model for Subspace Clustering
- ACTA ELECTRONICA SINICA Vol. 50, Issue 3, Pages: 718-725(2022)
- 作者机构：
  
  1.河北大学网络空间安全与计算机学院，河北保定 071002
  2.河北省机器视觉工程研究中心，河北保定 071002
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201146
  CLC： TP301.6
- Received：18 October 2020，
  
  Revised：2021-03-30，
  
  Published：25 March 2022
- 稿件说明：
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李凯,张可心.结构α-熵的加权高斯混合模型的子空间聚类[J].电子学报,2022,50(03):718-725.

LI Kai,ZHANG Ke-xin.Structural α-Entropy Weighting Gaussian Mixture Model for Subspace Clustering[J].ACTA ELECTRONICA SINICA,2022,50(03):718-725.
李凯,张可心.结构α-熵的加权高斯混合模型的子空间聚类[J].电子学报,2022,50(03):718-725. DOI： 10.12263/DZXB.20201146.

LI Kai,ZHANG Ke-xin.Structural α-Entropy Weighting Gaussian Mixture Model for Subspace Clustering[J].ACTA ELECTRONICA SINICA,2022,50(03):718-725. DOI： 10.12263/DZXB.20201146.

摘要

利用信息熵或模糊熵确定子空间聚类中每个簇的不同特征，较好地解决了高维数据的子空间聚类.为了进一步提高聚类算法的性能，将权向量的负结构

-熵引入到高斯混合模型中，获得了结构

-熵的加权高斯混合的子空间聚类模型，提出了结构

-熵的加权高斯混合模型的子空间聚类算法SEWMM(Structural

-Entropy Weighting Mixture Model)，该算法不仅可以发现高维数据空间中位于不同子空间的簇，而且能够获得子空间中具有不同形状体积的簇.同时，进一步分析了算法的收敛性与时间复杂性.通过选取UCI(University of California

Irvine) 标准数据集及图像数据集，对提出的算法SEWMM进行了实验，并与一些典型的聚类算法进行了比较，表明了提出的算法在总体性能上具有一定的提升.

Abstract

Using information entropy or fuzzy entropy to determine the different features of each cluster for subspace clustering

subspace clustering for high dimensional data is solved very well. For further improving performance of clustering algorithm

negative structural

α‑

entropy with weight vector is introduced into the Gaussian mixture model to obtain a structural

α‑

entropy weighting mixture model of subspace clustering. Based on this

the structural

α‑

entropy weighting mixture model subspace clustering algorithm(SEWMM) is derived theoretically

which can not only discover clusters in different subspaces in high dimensional data space

but also can discover clusters with various shape volumes in subspaces. And convergence and time complexity of algorithm are further analyzed. In the experiment

compared with some representative algorithms

the proposed algorithm SEWMM is tested on UCI(University of California

Irvine) standard data sets and image data sets. It shows the proposed algorithm has a certain improvement in the overall performance.

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references

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