论文标题
学习dags而不施加过环
Learning DAGs without imposing acyclicity
论文作者
论文摘要
我们探索是否可以从数据中学习有向的无环图（DAG），而无需明确施加无环的约束。特别是，对于高斯分布，我们将结构学习作为一个稀疏的基质分解问题，并从经验上表明，求解$ \ ell_1 $ - 二元化优化的产量，以良好的恢复真实的图形，并且通常几乎是DAG图。此外，这种方法在计算上是有效的，并且不像经典结构学习算法那样受到组合复杂性的爆炸影响。
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an $\ell_1$-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.
