Internet platforms provide new ways for people to share experiences, generating massive amounts of data related to various real-world concepts. In this paper, we present an event detection framework to discover real-world events from multiple data domains, including online news media and social media. As multi-domain data possess multiple data views that are heterogeneous, initial dictionaries consisting of labeled data samples are exploited to align the multi-view data. Furthermore, a shared multi-view data representation (SMDR) model is devised, which learns underlying and intrinsic structures shared among the data views by considering the structures underlying the data, data variations, and informativeness of dictionaries. SMDR incorpvarious constraints in the objective function, including shared representation, low-rank, local invariance, reconstruction error, and dictionary independence constraints. Given the data representations achieved by SMDR, class-wise residual models are designed to discover the events underlying the data based on the reconstruction residuals. Extensive experiments conducted on two real-world event detection datasets, i.e., Multi-domain and Multi-modality Event Detection dataset, and MediaEval Social Event Detection 2014 dataset, indicating the effectiveness of the proposed approaches.

中文翻译：

---

用于多域事件检测的共享多视图数据表示


互联网平台为人们分享经验提供了新的方式，产生与各种现实世界概念相关的大量数据。在本文中，我们提出了一个事件检测框架，用于从多个数据域（包括在线新闻媒体和社交媒体）发现现实世界事件。由于多域数据拥有异构的多个数据视图，因此利用由标记数据样本组成的初始字典来对齐多视图数据。此外，设计了一种共享多视图数据表示（SMDR）模型，该模型通过考虑数据底层结构、数据变化和字典的信息性来学习数据视图之间共享的底层和内在结构。 SMDR 目标函数中的各种约束，包括共享表示、低秩、局部不变性、重构误差和字典独立性约束。给定 SMDR 实现的数据表示，分类残差模型旨在根据重建残差发现数据背后的事件。在两个真实世界事件检测数据集（即多域和多模态事件检测数据集和 MediaEval 社交事件检测 2014 数据集）上进行了大量实验，表明了所提出方法的有效性。