机译:通过锚定图和ℓ_(2,1)-norm正则化快速进行无监督特征选择
The Xi’an Research Institute of Hi-Tech;
The Xi’an Research Institute of Hi-Tech,The Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University;
The Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University;
The School of Information Engineering, Guangdong University of Technology;
The School of Electronic and Information Engineering, Xi’an Jiaotong University;
Unsupervised feature selection; Anchor graph; ℓ2,1-norm;
机译:通过L_(2,1)-范数最小化进行无监督的最大余量特征选择
机译:基于l(2,1)范数正则化的多核联合非线性特征选择和过采样用于不平衡数据分类
机译:l(2,1)规范化的费舍尔准则,用于最佳特征选择
机译:ℓ_(2,1)-无监督学习的范数正则化鉴别特征选择
机译:通过在无人监督的设置中进行功能选择来发现类。
机译:基于L1-Norm正规化的脑电图信号特征选择早期筛选自闭症谱系障碍的儿童
机译:通过l2,1-范数正则化进行鲁棒分类的最佳特征选择