Compressive nonparametric graphical model selection for time series
Compressive nonparametric graphical model selection for time series
复制标题
时间序列的压缩非参数图形模型选择
DOI：
10.1109/icassp.2014.6853700
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
F. Hlawatsch
中科院分区：
文献类型：
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作者：
A. Jung;Reinhard Heckel;H. Bölcskei;F. Hlawatsch
提出了一种从有限长观测数据推断高维离散时间高斯向量随机过程的条件独立性图的方法。我们的方法不依赖于向量随机过程的参数模型(例如，自回归模型)；相反，它仅假设某些谱平滑特性。所提出的推理方案是压缩的，因为它适用于样本大小(远远)小于标量过程组件的数量。我们为我们的方法以高概率正确识别CIG提供了分析条件。
We propose a method for inferring the conditional independence graph (CIG) of a high-dimensional discrete-time Gaussian vector random process from finite-length observations. Our approach does not rely on a parametric model (such as, e.g., an autoregressive model) for the vector random process; rather, it only assumes certain spectral smoothness properties. The proposed inference scheme is compressive in that it works for sample sizes that are (much) smaller than the number of scalar process components. We provide analytical conditions for our method to correctly identify the CIG with high probability.