Multi-Directional Multi-Label Learning

Danyang Wu, Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In multi-label learning, the key problem is to capture the relationships between multiple labels, including proximities and unconformities. In this paper, we consider the relationships among multiple labels from multi-directions, including utilizing discriminative classifier, proposing a general hierarchical constraint and proximity correlation, meanwhile combining low-rank constraint, to infer a novel Multi-Directional Multi-Label learning (MDML) model. To optimize the problems involved in to the proposed models, we develop an iterative algorithms based on the alternating direction method of multipliers (ADMM) algorithm. In the simulations, the experimental results on 4 popular benchmark datasets demonstrate the superiorities of MDML model.

Original languageEnglish
Article number108143
JournalSignal Processing
Volume187
DOIs
StatePublished - Oct 2021

Keywords

  • Image Processing
  • Low-Rank Learning
  • Multi-Label Learning

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