火力与指挥控制2017，Vol.42Issue(3)：75-79,5.
改进的稀疏子空间聚类算法
Improved Sparse Subspace Clustering Algorithm
摘要
Abstract
Based on the existing theory of sparse subspace clustering algorithm,a modified sparse subspace clustering algorithm is put forward:iterative weighted sparse subspace clustering algorithm.In order to cluster data,sparse subspace clustering algorithm clusters high-dimensional data to different subspaces by solving minimization algorithm and applying spectral clustering.Iterative algorithm has more fair punishment value then the traditional algorithm,with balancing the influence of magnitude of data.The algorithm is applied to the sparse subspace clustering to improve the traditional sparse subspace clustering performance for data. Simulation experiment recognizing and classify Yale B face data image.The clustering effect is very good,proving the superiority of the improved algorithm.关键词
稀疏子空间聚类/迭代加权/谱聚类算法/人脸识别Key words
sparse subspace clustering/iterative weighted/spectral clustering algorithms/face regulation分类
信息技术与安全科学引用本文复制引用
张彩霞,胡红萍,白艳萍..改进的稀疏子空间聚类算法[J].火力与指挥控制,2017,42(3):75-79,5.基金项目
国家自然科学基金(61275120) （61275120）
2014年校自然科学基金资助项目 （）
