Imputation of coupled tensors and GRAPHS

V. N. Ioannidis, A. S. Zamzam, Georgios B Giannakis, Nikolaos Sidiropoulos

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

1 Scopus citations

Abstract

Joint analysis of data from different sources can potentially improve one's ability to reveal latent structure in heterogeneous datasets. For instance, social network activities and user demographic information can be leveraged to improve recommendations. However, the incompleteness and heterogeneity of the data challenge joint factorization of multiple datasets. Aspiring to address these challenges, the coupled graph tensor factorization model accounts for side information available in the form of correlation matrices or graphs. Here, a novel ADMM-based approach is put forth to impute missing entries and unveil hidden structure in the data. The iterative solver enjoys closed-form updates that result in reduced computational complexity. Numerical tests with synthetic and real data corroborate the merits of the proposed method relative to competing alternatives.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1331-1335
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

Bibliographical note

Funding Information:
The work in this paper was supported by NSF grants 171141, 1500713, and 1442686.

Keywords

  • Imputation
  • Non-negative factorization
  • Parallel factor (PARAFAC)/ canonical polyadic decomposition (CPD) model
  • Recommender systems

Fingerprint Dive into the research topics of 'Imputation of coupled tensors and GRAPHS'. Together they form a unique fingerprint.

Cite this