一种预测人类对图形布局偏好的机器学习方法
A Machine Learning Approach for Predicting Human Preference for Graph Layouts
摘要：
由于人类大脑中高度复杂的视觉感知和认知系统，了解人类喜欢什么样的图形布局以及为什么喜欢图形布局是非常重要和具有挑战性的。在本文中，我们提出了第一个机器学习方法来预测人类的偏好图布局。一般来说，带有人类偏好标签的数据集是有限的，不足以训练深度网络。为了解决这个问题，我们通过采用迁移学习方法来训练我们的深度学习模型，例如，利用质量度量，例如基于形状的度量、边缘交叉和应力，这些被示出为与人类对图布局的偏好相关。使用地面真实人类偏好数据集的实验结果表明，我们的模型可以成功地预测人类对图形布局的偏好。据我们所知，这是第一种使用人类偏好实验数据预测图形布局定性评价的方法。
英文摘要：
Understanding what graph layout human prefer and why they prefer is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts. In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are shown to be correlated to human preference on graph layouts. Experimental results using the ground truth human preference data sets show that our model can successfully predict human preference for graph layouts. To our best knowledge, this is the first approach for predicting qualitative evaluation of graph layouts using human preference experiment data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
