一种改进的最小二乘回归子空间分割方法

doi:10.3969/j.issn.1006-2475.2019.05.022

计算机与现代化

一种改进的最小二乘回归子空间分割方法

(1.江南大学物联网工程学院,江苏无锡214122； 2.镇江高等专科学校,江苏镇江212028；
3.无锡科技职业技术学院物联网与软件技术学院，江苏无锡214028)

收稿日期:2018-10-17 出版日期:2019-05-14 发布日期:2019-05-14
作者简介:蔡晓云(1976-),男,江苏丹阳人,副教授,硕士,研究方向:人工智能,E-mail: dysfcxy@126.com; 尹贺峰(1989-),男,河南平舆人,博士,研究方向:人工智能,模式识别; 傅文进(1993-),男,江苏盐城人,硕士,研究方向:人工智能,模式识别; 赵航涛(1972-),男,江苏无锡人,副教授,硕士,研究方向:人工智能,物联网。
基金资助:
镇江市软科学研究计划项目(RK2017027)

An Improved Subspace Segmentation Method Based on Least Squares Regression

(1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China； 2. Zhenjiang College, Zhenjiang 212028, China；
3. School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi 214028, China)

Received:2018-10-17 Online:2019-05-14 Published:2019-05-14

摘要/Abstract

摘要： 最小二乘回归(LSR)算法是一种常见的子空间分割方法，由于LSR具有解析解，因此它的聚类性能较高。然而LSR算法是应用谱聚类方法聚类数据，谱聚类方法初始化聚类中心是随机的，会影响后面的聚类效果。针对这一问题，提出一种基于聚类中心局部密度和距离这2个特点的改进的LSR算法（LSR-DC）。在Extended Yale B数据集上进行实验，结果表明，该算法有较高的聚类精度，具有一定的鲁棒性，优于现有LSR等子空间分割方法。

关键词: 最小二乘回归, 子空间分割, 聚类, 局部密度, 距离

Abstract: Least Squares Regression (LSR) is a common approach for subspace segmentation, it is very efficient due to a closed form solution. However, spectral clustering is exploited in LSR to obtain the final segmentation results. The drawback of spectral clustering is that it randomly initializes the cluster centers, which may undermine the subsequent clustering performance. In order to tackle this problem, this paper presents an improved LSR algorithm (LSR-DC) based on two characteristics of cluster centers, i.e. local density and distance. Experimental results on the Extended Yale B database show that LSR-DC is robust and is superior to the existing LSR subspace segmentation methods.

Key words: least squares regression, subspace segmentation, clustering, local density, distance

中图分类号:

TP391

蔡晓云1,2，尹贺峰1，傅文进1，赵航涛1,3. 一种改进的最小二乘回归子空间分割方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2019.05.022.

CAI Xiao-yun1,2, YIN He-feng1, FU Wen-jin1, ZHAO Hang-tao1,3. An Improved Subspace Segmentation Method Based on Least Squares Regression[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.05.022.

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一种改进的最小二乘回归子空间分割方法

An Improved Subspace Segmentation Method Based on Least Squares Regression

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