论文标题
通过文本和天气数据通过联合表示学习无监督的恶劣天气检测
Unsupervised Severe Weather Detection Via Joint Representation Learning Over Textual and Weather Data
论文作者
论文摘要
在观察现象时，严重的病例或异常通常以偏离预期数据分布的特征。但是，非差异数据样本也可能导致严重的结果。在无监督的恶劣天气检测的情况下，这些数据样本可能导致错误预测，因为经常不会直接将恶劣天气的预测视为特征。我们认为，结合外部或辅助信息，例如外部任务的结果或观察结果，可以改善无监督检测算法的决策界限。在本文中，我们通过学习增强和线性可分离的潜在表示来提高聚类方法来检测恶劣天气的有效性。我们对三种个人恶劣天气，即风暴，洪水和龙卷风爆发进行评估。
When observing a phenomenon, severe cases or anomalies are often characterised by deviation from the expected data distribution. However, non-deviating data samples may also implicitly lead to severe outcomes. In the case of unsupervised severe weather detection, these data samples can lead to mispredictions, since the predictors of severe weather are often not directly observed as features. We posit that incorporating external or auxiliary information, such as the outcome of an external task or an observation, can improve the decision boundaries of an unsupervised detection algorithm. In this paper, we increase the effectiveness of a clustering method to detect cases of severe weather by learning augmented and linearly separable latent representations.We evaluate our solution against three individual cases of severe weather, namely windstorms, floods and tornado outbreaks.
