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

Hongteng Xu, Dixin Luo, Lawrence Carin

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

4 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial IntelligenceThomas.schiex@toulouse.inra.fr
Pages2905-2911
Number of pages7
ISBN (Print)9780999241127
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

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