Learning Representation from Multiple Media Domains for Enhanced Event Discovery

Zhenguo YANG*, Qing LI, Haoran XIE, Qi WANG, Wenyin LIU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)

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.
Original languageEnglish
Article number107640
JournalPattern Recognition
Volume110
Early online date10 Sep 2020
DOIs
Publication statusE-pub ahead of print - 10 Sep 2020

Keywords

  • Data representation learning
  • Event detection
  • Social media
  • Multi-modality data

Fingerprint Dive into the research topics of 'Learning Representation from Multiple Media Domains for Enhanced Event Discovery'. Together they form a unique fingerprint.

  • Cite this