Deep co-attention network for multi-view subspace learning

Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topology of the underlying social network and the content of the users' posts; in the medical domain, multiple views could be X-ray images taken at different poses. To date, various techniques have been proposed to achieve promising results, such as canonical correlation analysis based methods, etc. In the meanwhile, it is critical for decision-makers to be able to understand the prediction results from these methods. For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction. However, state-of-the-art techniques usually suffer from the inability to utilize the complementary information of each view and to explain the predictions in an interpretable manner. To address these issues, in this paper, we propose a deep co-attention network for multi-view subspace learning, which aims to extract both the common information and the complementary information in an adversarial setting and provide robust interpretations behind the prediction to the end-users via the co-attention mechanism. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation by incorporating the classifier into our model. This improves the quality of latent representation and accelerates the convergence speed. Finally, we develop an efficient iterative algorithm to find the optimal encoders and discriminator, which are evaluated extensively on synthetic and real-world data sets. We also conduct a case study to demonstrate how the proposed method robustly interprets the predictions on an image data set.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery, Inc
Pages1528-1539
Number of pages12
ISBN (Electronic)9781450383127
DOIs
StatePublished - Apr 19 2021
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: Apr 19 2021Apr 23 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Conference

Conference2021 World Wide Web Conference, WWW 2021
Country/TerritorySlovenia
CityLjubljana
Period4/19/214/23/21

Keywords

  • Attention Mechanism
  • Interpretable Machine Learning
  • Multi-view Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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