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基于最大范数的低秩稀疏分解模型

王斯琪 冯象初 张瑞 李小平

王斯琪, 冯象初, 张瑞, 李小平. 基于最大范数的低秩稀疏分解模型[J]. 电子与信息学报, 2015, 37(11): 2601-2607. doi: 10.11999/JEIT150468
引用本文: 王斯琪, 冯象初, 张瑞, 李小平. 基于最大范数的低秩稀疏分解模型[J]. 电子与信息学报, 2015, 37(11): 2601-2607. doi: 10.11999/JEIT150468
Wang Si-qi, Feng Xiang-chu, Zhang Rui, Li Xiao-ping. Low-rank Sparse Decomposition Model Based on Max-norm[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2601-2607. doi: 10.11999/JEIT150468
Citation: Wang Si-qi, Feng Xiang-chu, Zhang Rui, Li Xiao-ping. Low-rank Sparse Decomposition Model Based on Max-norm[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2601-2607. doi: 10.11999/JEIT150468

基于最大范数的低秩稀疏分解模型

doi: 10.11999/JEIT150468
基金项目: 

国家自然科学基金(61271294, 61472303)和中央高校基本科研业务费专项资金(NSIY21)

Low-rank Sparse Decomposition Model Based on Max-norm

Funds: 

The National Natural Science Foundation of China (61271294, 61472303)

  • 摘要: 为了更好地解决高维数据矩阵低秩稀疏分解问题,该文提出以Max-范数凸化秩函数的Max极小化模型,并给出该模型的相应算法。在对新模型计算复杂性分析的基础上,该文进一步提出了Max约束模型,改进模型不仅在分解问题中效果良好,且相应的投影梯度算法具有更强的时效性。实验结果表明,该文提出的两组模型对于低秩稀疏分解问题均行之有效。
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出版历程
  • 收稿日期:  2015-04-22
  • 修回日期:  2015-07-08
  • 刊出日期:  2015-11-19

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