Inverse Ising Inference Using All the Data
Inverse Ising Inference Using All the Data
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DOI：
10.1103/physrevlett.108.090201
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发表时间：
2012-03-01
影响因子：
8.6
通讯作者：
Ekeberg, Magnus
中科院分区：
文献类型：
--
作者：
Aurell, Erik;Ekeberg, Magnus
我们表明，一种基于逻辑回归的方法，使用所有的数据，解决了逆伊辛问题远远优于平均场计算仅依赖于样本成对相关函数，同时仍然计算上可行的数百个节点。重建的最大改进发生在强相互作用中。使用两个例子，一个稀释的Sherrington-Kirkpatrick模型和一个二维晶格，我们还表明，相互作用的拓扑结构可以恢复从几个样本具有良好的精度和使用l（1）正则化是有益的，在这个过程中，推动推理能力进一步进入低温制度。
We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l(1) regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.