机译:降维联合图嵌入和稀疏回归的框架
Shenzhen Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;
Algorithm design and analysis; Educational institutions; Electronic mail; Joints; Optimization; Principal component analysis; Vectors; $L_{2, 1}$ -norm; Graph embedding; feature selection; sparse regression; subspace learning;
机译:联合嵌入学习和稀疏回归:无监督特征选择的框架
机译:鲁棒的联合稀疏嵌入以减少维数
机译:学习在图上传播标签:用于半监督的高光谱降维的迭代多任务回归框架
机译:稀疏嵌入:稀疏性促进降维的框架
机译:稀疏的偏最小二乘回归,可同时进行降维和变量选择,并应用于高维基因组数据
机译:eeg特征选择通过带有关节稀疏性的堆叠深嵌入回归
机译:联合图嵌入和稀疏回归的降维框架