Large Scale Tensor Factorization via Parallel Sketches

Bo Yang, Ahmed Zamzam, Nicholas Sidiropoulos

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

Abstract

Tensor factorization methods have recently gained increased popularity. A key feature that renders tensors attractive is the ability to directly model multi-relational data. In this work, we propose ParaSketch, a parallel tensor factorization algorithm that enables massive parallelism, to deal with large tensors. The idea is to compress the large tensor into multiple small tensors, decompose each small tensor in parallel, and combine the results to reconstruct the desired latent factors. Prior art in this direction entails potentially very high complexity in the (Gaussian) compression and final combining stages. Adopting sketching matrices for compression, the proposed method enjoys a dramatic reduction in compression complexity, and features a much lighter combining step. Moreover, theoretical analysis shows that the compressed tensors inherit latent identifiability under mild conditions, hence establishing correctness of the overall approach. Numerical experiments corroborate the theory and demonstrate the effectiveness of the proposed algorithm.

Original languageAmerican English
Pages (from-to)365-378
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number1
DOIs
StatePublished - 1 Jan 2022

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-82847

Keywords

  • block term decomposition
  • identifiability
  • PARAFAC
  • sketching
  • Tensor factorization

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