论文标题
使用神经网络上的拓扑分析进行互动检测
Towards Interaction Detection Using Topological Analysis on Neural Networks
论文作者
论文摘要
检测输入特征之间的统计相互作用是一项至关重要且具有挑战性的任务。最近的进步表明，可以从训练有素的神经网络中提取学识渊博的互动。还可以观察到，在神经网络中，任何相互作用的特征都必须遵循与共同隐藏单元的强度加权连接。在观察过程中，在本文中，我们建议通过分析神经网络中的连通性来研究相互作用检测问题。特别是，我们提出了一种基于广受欢迎的持续同源性理论来量化相互作用强度的新措施。基于此度量，开发了持久性相互作用检测〜（PID）算法以有效检测相互作用。我们提出的算法在具有不同的超参数的几个合成和现实世界数据集上进行了许多相互作用检测任务进行评估。实验结果验证了PID算法的表现优于最新基准。
Detecting statistical interactions between input features is a crucial and challenging task. Recent advances demonstrate that it is possible to extract learned interactions from trained neural networks. It has also been observed that, in neural networks, any interacting features must follow a strongly weighted connection to common hidden units. Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks. Specially, we propose a new measure for quantifying interaction strength, based upon the well-received theory of persistent homology. Based on this measure, a Persistence Interaction detection~(PID) algorithm is developed to efficiently detect interactions. Our proposed algorithm is evaluated across a number of interaction detection tasks on several synthetic and real world datasets with different hyperparameters. Experimental results validate that the PID algorithm outperforms the state-of-the-art baselines.
