Bryan Andrews, Joseph Ramsey, Rubén Sánchez-Romero, Jazmin Camchong, Erich Kummerfeld
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引用次数: 0
摘要
学习图形条件独立性结构是一个重要的机器学习问题,也是因果关系发现的基石。然而,学习算法的准确性和执行时间通常难以扩展到具有数百个高度连接变量的问题--例如,从 fMRI 数据中恢复大脑网络。在这种模式下,我们引入了最佳秩分搜索(BOSS)和生长收缩树(GST)来学习有向无环图(DAG)。BOSS 贪婪地搜索变量的排列组合,利用 GST 从排列组合中构建 DAG 并为其评分。GST 可以有效地缓存分数,以消除冗余计算。BOSS 在准确性和执行时间方面都达到了最先进的水平,在各种条件下都能与各种组合学习算法和基于梯度的学习算法相媲美。为了证明其实用性,我们将 BOSS 应用于两组静息态 fMRI 数据:从随机经验 fMRI 皮层信号中得出的带有伪经验噪声分布的模拟数据,以及从处理成皮质区块的 3T fMRI 扫描中得到的临床数据。BOSS 可在 TETRAD 项目中使用,该项目包括 Python 和 R 封装程序。
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables-for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.