摘要
在现有的稀疏子空间聚类算法理论基础上给出两种稀疏子空间聚类优化算法:稀疏线性子空间聚类和稀疏仿射子空间聚类。这两种优化算法针对不同的数据集会有不同的聚类效果。通过稀疏表达得到不同的稀疏系数矩阵,把稀疏系数矩阵应用到较为简单的改进的正则化谱聚类算法中实现聚类。应用Yale B数据对人脸图像进行识别分类得出:采用稀疏线性子空间聚类算法优于稀疏仿射子空间聚类算法;在算法执行时间上和算法聚类错误率比传统的稀疏子空间聚类较为快速高效。
We offer two kinds of sparse subspace clustering optimization algorithm,sparse linear space clustering and sparse affine subspace clustering,based on the existing theory of sparse subspace clustering algorithm.For different data gathering,these two kinds of optimization algorithm has different clustering results.In this paper,different sparse coefficient matrix by sparse expression is obtained.In order to achieve cluster,the sparse coefficient matrix is applied to relatively simple regularization of spectral clustering algorithm.Application of Yale B data,we recognize and classify face image:using sparse linear space clustering algorithm is better than the sparse affine subspace clustering algorithm;Comparing with the traditional sparse subspace clustering,it is more fast and efficient in the time of execution and error rate of algorithm.
作者
张彩霞
胡红萍
白艳萍
ZHANG Cai-xia;HU Hong-ping;BAI Yan-ping(Faculty of Science,Department of Maths,North University of China,Taiyuan 030051,China)
出处
《火力与指挥控制》
CSCD
北大核心
2017年第4期29-32,共4页
Fire Control & Command Control
基金
国家自然科学基金(61275120)
2014年校自然科学基金资助项目
关键词
子空间聚类
稀疏子空间聚类
谱聚类算法
人脸识别
subspace clustering
sparse subspace clustering
spectral clustering algorithms
face regulation
