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

Riku Hashimoto, Hiroyuki Kasai

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5420-5424
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5
イベント2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
継続期間: 2020 5 42020 5 8

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

Conference

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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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