计算机技术与发展Issue(11)：131-134,4.DOI:10.3969/j.issn.1673-629X.2014.11.033
稀疏子空间聚类的惩罚参数自调整交替方向法
Alternating Direction Method of Self-adjusting Penalty Parameters of Sparse Subspace Clustering
摘要
Abstract
Sparse subspace clustering uses the sparse representation of vectors lying on a union of subspace to cluster the data into separate subspaces. The key of this algorithm is to find the optimal sparse solution. Alternating Direction Method ( ADM) is applied to solve sparse problem in this paper. ADM divides the complex problem into simple and effectively solving sub-problem to achieve optimal speed. In the process of the ADM solving,the penalty factor is constant. In this paper,a penalty factor self-adjusting strategy is proposed, according to the each iterative information,adjust the penalty factor parameters. The experiment based on motion division data and Hop-kins database shows that the proposed method has great improvement in iteration times and computing time compared with traditional al-gorithms,is also valid for noisy data.关键词
子空间聚类/稀疏表示/L1范数正则化/交替方向法Key words
subspace clustering/sparse representation/L1 norm regularization/alternating direction method分类
信息技术与安全科学引用本文复制引用
姚刚,杨敏..稀疏子空间聚类的惩罚参数自调整交替方向法[J].计算机技术与发展,2014,(11):131-134,4.基金项目
江苏省自然科学基金(BK2011758) （BK2011758）
南京邮电大学攀登计划(NY212066) （NY212066）
