Abstract In this paper, we focus on event discovery by utilizing data distributed in multiple media domains, such as news media and social media. To this end, we propose an in-domain and cross-domain Laplacian regularization (ICLR) model, which can learn effective data representation for both textual news reports contributed by journalists in news media domain, and image posts shared by amateur users in social media domain. The achieved data representation can be used by classification and clustering strategies for existing and new event discovery, respectively. More specifically, ICLR constructs respective Laplacian regularization terms considering the property of inter-domain and intra-domain label consistency, which can be optimized by employing an alternating optimization strategy with theoretical guarantee for convergence. In particular, we collect and release a multi-domain and multimodal dataset for evaluations and public use.

中文翻译：

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从多个媒体域学习表示​​以增强事件发现

摘要 在本文中，我们通过利用分布在多个媒体领域（如新闻媒体和社交媒体）中的数据来关注事件发现。为此，我们提出了一个域内和跨域拉普拉斯正则化 (ICLR) 模型，该模型可以为新闻媒体领域的记者提供的文本新闻报道和社交媒体中业余用户共享的图像帖子学习有效的数据表示领域。获得的数据表示可以分别用于现有和新事件发现的分类和聚类策略。更具体地说，ICLR 考虑域间和域内标签一致性的特性构造了各自的拉普拉斯正则化项，可以通过采用具有收敛理论保证的交替优化策略进行优化。特别是，