A unified framework of latent feature learning in social media

Zhaoquan Yuan, Jitao Sang, Changsheng Xu, Yan Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. Representation learning has been a fundamental problem in machine learning, and widely recognized as critical to the performance of end tasks. In this paper, we provide evidence that specially learned features will addresses the diverse, heterogeneous, and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a unified framework of latent feature learning on social media. To address the noisy, diverse, heterogeneous, and interconnected characteristics of social media data, the popular deep learning is employed due to its excellent abstract abilities. In particular, we instantiate the proposed framework by (1) designing a novel relational generative deep learning model to solve the social media link analysis task, and (2) developing a multimodal deep learning to lambda rank model towards the social image retrieval task. We show that the derived latent features lead to improvement in both of the social media tasks.
Original languageEnglish
Article number6810890
Pages (from-to)1624-1635
Number of pages12
JournalIEEE Transactions on Multimedia
Issue number6
Publication statusPublished - 1 Oct 2014


  • Deep learning
  • feature learning
  • india buffet process
  • social media

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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