Sequential semi-orthogonal multi-level NMF with negative residual reduction for network embedding

Riku Hashimoto, Hiroyuki Kasai

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

3 Citations (Scopus)

Abstract

Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed multi-level nonnegative matrix factorization (NMF)-based approach has exhibited superior performance on network analysis, it is adversely affected by performance degradation because of discarded negative residual and redundant base selection throughout sequential multiple factorization processes. To alleviate this shortcoming, this paper presents a proposal of a sequential semi-orthogonal NMF with negative residual reduction for the network embedding (SSO-NRR-NMF). The proposed approach reduces the negative residuals to be discarded, and avoids redundant bases with a semi-orthogonal constraint. Numerical evaluations conducted using several real-world datasets demonstrate the effectiveness of the proposed SSO-NRR-NMF.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5420-5424
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 2020 May
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 2020 May 42020 May 8

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period20/5/420/5/8

Keywords

  • Negative residual reduction NMF
  • Network embedding
  • Nonnegative matrix factorization (NMF)
  • Sequential semi-orthogonal NMF

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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