抽样子空间约束改进大数据谱聚类算法

聂茹

doi:10.11959/j.issn.1000-0801.2018277

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抽样子空间约束改进大数据谱聚类算法

研究与开发 | 更新时间：2024-06-05

- 抽样子空间约束改进大数据谱聚类算法
- Improved large data spectral clustering algorithm based on sampling subspace constraint
- 电信科学 2018年34卷第11期页码：41-47
- 作者机构：
- 作者简介：
  
  [ "聂茹（1982-），女，华南理工大学广州学院电子信息工程学院讲师、教学研究室副主任，主要研究方向为智能信息处理、智能控制、电力电子与自动控制等。" ]
- 基金信息：
  
  广东省教育厅青年创新人才基金资助项目;Youth Innovation Talent Project of Guangdong Provincial Department of Education(2016KQNCX227)
- DOI：10.11959/j.issn.1000-0801.2018277
  中图分类号： TP181
- 网络出版日期：2018-11，
  
  纸质出版日期：2018-11-20
- 稿件说明：
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聂茹. 抽样子空间约束改进大数据谱聚类算法[J]. 电信科学, 2018,34(11):41-47.

Ru NIE. Improved large data spectral clustering algorithm based on sampling subspace constraint[J]. Telecommunications science, 2018, 34(11): 41-47.
聂茹. 抽样子空间约束改进大数据谱聚类算法[J]. 电信科学, 2018,34(11):41-47. DOI： 10.11959/j.issn.1000-0801.2018277.

Ru NIE. Improved large data spectral clustering algorithm based on sampling subspace constraint[J]. Telecommunications science, 2018, 34(11): 41-47. DOI： 10.11959/j.issn.1000-0801.2018277.

摘要

在分析经典谱聚目标函数与加权核k-means目标函数等价基础上，设计了一种基于抽样子空间约束的改进大规模数据谱聚类算法，算法通过加权核k-means迭代优化避免矩阵特征分解的大量资源被占用，通过数据抽样及聚类中心的子空间约束，避免全部核矩阵都被使用，从而降低经典算法的时间空间复杂度。理论分析和实验结果表明，改进算法保持与经典算法相近聚类精度，提高了聚类效率，验证了改进算法的有效性。

Abstract

On the basis of analyzing the equivalent function of the objective function of classical spectral clustering algorithm and the weighted kernel k-means objective function

an improved large-scale data spectrum clustring algorithm based on sampling subspace constraint was designed

the weighted kernel k-means iterative optimization was used to avoid the large resource consumption of Laplacian matrix feature decomposition

and by using data sampling and constraining the cluster center to the subspace generated by the sampling points

the use of all kernel matrices was avoided

thereby reducing the time-space complexity of classical algorithms.Theoretical analysis and experimental results show that the improved algorithm can greatly improve the clustering efficiency on the basis of maintaining similar clustering accuracy with the classic algorithm and verify the effectiveness of the proposed algorithm.

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