《厦门大学学报（自然科学版）》

针对高维数据的非线性特性会降低最小二乘回归(LSR)子空间聚类的性能,提出两阶段LSR(TLSR)子空间聚类方法.该方法利用LSR的表示系数定义局部信息惩罚项,构造局部约束LSR方法.在8个数据集上的实验表明该方法适合高维数据的聚类.

Since the nonlinear characteristic of high-dimensional data reduced the performance of least square regression subspace clustering,a two-stage least square regression subspace clustering method is proposed.In the proposed method,the representation coefficient of least square regression to define local information is used.A local constrained least square regression method is constructed by using the local penalty term.Experiments on eight data sets show that the method is suitable for high-dimensional data clustering.

引言
1 子空间聚类方法概述
2 TLSR方法
3 实验分析
4 结论

表1 数据集描述<br/>Tab.1 Summary of the data sets

表1 数据集描述
Tab.1 Summary of the data sets

表2 聚类准确率对比<br/>Tab.2 Clustering accuracy comparison %

表2 聚类准确率对比
Tab.2 Clustering accuracy comparison %

图1 不同λ和γ下TLSR在8个数据集上的聚类准确率<br/>Fig.1 Clustering accuracy of TLSR on 8 datasets with different λ and γ

图1 不同λ和γ下TLSR在8个数据集上的聚类准确率
Fig.1 Clustering accuracy of TLSR on 8 datasets with different λ and γ

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备注

引言

1 子空间聚类方法概述

2 TLSR方法

3 实验分析

4 结论

学报简介

备注

引言

1 子空间聚类方法概述

2 TLSR方法

3 实验分析

4 结 论

学报简介

4 结论