Online continuous-time tensor factorization based on pairwise interactive point processes

Published

Conference Paper

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. A continuous-time tensor factorization method is developed for event sequences containing multiple “modalities.” Each data element is a point in a tensor, whose dimensions are associated with the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model accounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.

Duke Authors

Cited Authors

  • Xu, H; Luo, D; Carin, L

Published Date

  • January 1, 2018

Published In

Volume / Issue

  • 2018-July /

Start / End Page

  • 2905 - 2911

International Standard Serial Number (ISSN)

  • 1045-0823

International Standard Book Number 13 (ISBN-13)

  • 9780999241127

Citation Source

  • Scopus