Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
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DOI：
10.1162/neco_a_00589
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发表时间：
2013-04
影响因子：
2.9
通讯作者：
Song Liu;J. Quinn;Michael U Gutmann;Taiji Suzuki;Masashi Sugiyama
中科院分区：
文献类型：
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作者：
Song Liu;J. Quinn;Michael U Gutmann;Taiji Suzuki;Masashi Sugiyama
我们提出了一种新的方法来检测两组样本之间的马尔可夫网络结构的变化。我们不是简单地将两个马尔可夫网络模型分别拟合到两个数据集并找出它们的差异，而是通过估计马尔可夫网络模型的比率来直接学习网络结构的变化。这种密度比公式自然允许我们在网络结构变化中引入稀疏性，这有助于增强可解释性。此外，计算的归一化项，一个关键的瓶颈的朴素的方法，可以显着减轻。我们还给出了优化问题的对偶形式，这进一步降低了大规模马尔可夫网络的计算成本。通过实验，我们证明了我们的方法的实用性。
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov networks. Through experiments, we demonstrate the usefulness of our method.