Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels
type: Item
instance of: scholarly article
title: Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels
zbMATH Open document ID: 1436.68311
author: Jiang Hong Ma
author: Tommy W. S. Chow
published in: Information Sciences
publication date: 2020-07-16T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68R10
zbMATH DE Number: 7221609
zbMATH Keywords: label recovery
zbMATH Keywords: semi-supervised setting
zbMATH Keywords: missing labels
zbMATH Keywords: semantic structure
zbMATH Keywords: semantic correlation
describes a project that uses: RCV1
describes a project that uses: MULAN
describes a project that uses: BoosTexter
describes a project that uses: ML-KNN
MaRDI profile type: Publication
full work available at URL: j.ins.2017.08.061
OpenAlex ID: W2750785001
cites work: Q4802571
cites work: Q4459352
cites work: Sparse subspace clustering for data with missing entries and high-rank matrix completion
cites work: Joint Multilabel Classification With Community-Aware Label Graph Learning
cites work: MLSLR: multilabel learning via sparse logistic regression
cites work: Multiview Matrix Completion for Multilabel Image Classification
cites work: BoosTexter: A boosting-based system for text categorization
cites work: Multi-Label Image Categorization With Sparse Factor Representation
cites work: Semantic-Gap-Oriented Active Learning for Multilabel Image Annotation
cites work: ML-KNN: A lazy learning approach to multi-label learning
DOI: 10.1016/J.INS.2017.08.061
description: scientific article; zbMATH DE number 7221609