A super-atomic norm minimization approach to identifying sparse dynamical graphical models
A super-atomic norm minimization approach to identifying sparse dynamical graphical models
复制标题
识别稀疏动态图模型的超原子范数最小化方法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
O. Camps
中科院分区：
文献类型：
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作者：
Yin Wang;M. Sznaier;O. Camps
This paper considers the problem of identifying sparse dynamical graphical models from input/output data. Our main result shows that this problem can be recast into an expanded atomic-norm minimization framework that allows for enforcing block-sparsity. This approach leads to efficient algorithms capable of handling large data sets, unknown inputs and fragmented data records. These results are illustrated with several examples.
