A graph learning algorithm based on gaussian markov random fields and minimax concave penalty

Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega

研究成果: Conference article査読

1 被引用数 (Scopus)

抄録

This paper presents a graph learning framework to produce sparse and accurate graphs from network data. While our formulation is in- spired by the graphical lasso, a key difference is the use of a noncon- vex alternative of the ℓ1 norm as well as a quadratic term to ensure overall convexity. Specifically, the weakly-convex minimax concave penalty (MCP) is used, which is given by subtracting the Huber func- tion from the ℓ1 norm, inducing a less-biased sparse solution than ℓ1. In our framework, the graph Laplacian is represented by a lin- ear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on the Moreau decomposition, the prob- lem can be solved by the primal-dual splitting method. An admis- sible choice of parameters for provable convergence is presented. Numerical examples show that the proposed method significantly outperforms its ℓ1-based counterpart for sparse grid graphs.

本文言語English
ページ(範囲)5410-5414
ページ数5
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2021-June
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
継続期間: 2021 6 62021 6 11

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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