论文标题
最佳运输的无监督分离器检测的元学习
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport
论文作者
论文摘要
自动化的机器学习已在监督分类和回归领域广泛研究和采用，但是在无监督的设置中的进展受到限制。我们提出了一种基于以前具有离群值的元学习的元学习来自动化离群值检测的新型方法。我们的前提是，最佳离群检测技术的选择取决于数据分布的固有属性。我们特别利用最佳运输，以找到具有最相似基础分布的数据集，然后应用证明最适合该数据分布的异常检测技术。我们评估了方法的鲁棒性，并发现它在无监督的离群值检测中的表现优于最先进的方法。这种方法也很容易被概括以使其他无监督的设置自动化。
Automated machine learning has been widely researched and adopted in the field of supervised classification and regression, but progress in unsupervised settings has been limited. We propose a novel approach to automate outlier detection based on meta-learning from previous datasets with outliers. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport in particular, to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our approach and find that it outperforms the state of the art methods in unsupervised outlier detection. This approach can also be easily generalized to automate other unsupervised settings.
