电子科技大学学报Issue(6)：899-904,6.DOI:10.3969/j.issn.1001-0548.2015.06.018
一种非负稀疏近邻表示的多标签学习算法
A Non-Negative Sparse Neighbor Representation for Multi-Label Learning Algorithm
摘要
Abstract
In order to avoid the influence of the nonlinear manifold structure in training data and preserve more discriminant information in the sparse representation based multi-label learning, a new multi-label learning algorithm based on non-negative sparse neighbor representation is proposed. First of all, thek-nearest neighbors among each class are found for the test sample. Secondly, based on non-negative the least absolute shrinkage and selectionator operator (LASSO)-type sparse minimization, the test sample is non-negative linearly reconstructed by thek-nearest neighbors. Then, the membership of each class for the test sample is calculated by using the reconstruction errors. Finally, the classification is performed by ranking these memberships. A fast iterative algorithm and its corresponding analysis of converging to global minimum are provided. Experimental results of multi-label classification on several public multi-label databases show that the proposed method achieves better performances than classical ML-SRC and ML-KNN.关键词
多标签学习/稀疏近邻表示/LASSO稀疏最小化/非负重构Key words
LASSO sparse minimization/multi-label learning/non-negative reconstruction/sparse neighbor representation分类
信息技术与安全科学引用本文复制引用
陈思宝,徐丹洋,罗斌..一种非负稀疏近邻表示的多标签学习算法[J].电子科技大学学报,2015,(6):899-904,6.基金项目
国家863项目(2014AA015104)；国家自然科学基金(61202228，61472002)；安徽省高校自然科学研究重点项目(KJ2012A004) （2014AA015104）
