Multi-instance multi-label learning with weak label

Authors:
Shu-Jun Yang;Yuan Jiang;Zhi-Hua Zhou
Affiliations:
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 18
Cited 1

Minimization methods for non-differentiable functions

Minimization methods for non-differentiable functions
A framework for multiple-instance learning

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence of a block coordinate descent method for nondifferentiable minimization

Journal of Optimization Theory and Applications
Building Text Classifiers Using Positive and Unlabeled Examples

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Convex multi-task feature learning

Machine Learning
Semi-supervised learning using label mean

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Letters: MIMLRBF: RBF neural networks for multi-instance multi-label learning

Neurocomputing
Transductive Multi-Instance Multi-Label learning algorithm with application to automatic image annotation

Expert Systems with Applications: An International Journal
Drosophila gene expression pattern annotation through multi-instance multi-label learning

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset

ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
A New SVM Approach to Multi-instance Multi-label Learning

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Multi-instance multi-label learning

Artificial Intelligence
Multi-label learning with incomplete class assignments

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-label hypothesis reuse

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Rank-loss support instance machines for MIML instance annotation

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised multi-instance multi-label learning for video annotation task

Proceedings of the 20th ACM international conference on Multimedia

Convex and scalable weakly labeled SVMs

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-Instance Multi-Label learning (MIML) deals with data objects that are represented by a bag of instances and associated with a set of class labels simultaneously. Previous studies typically assume that for every training example, all positive labels are tagged whereas the untagged labels are all negative. In many real applications such as image annotation, however, the learning problem often suffers from weak label; that is, users usually tag only a part of positive labels, and the untagged labels are not necessarily negative. In this paper, we propose the MIMLwel approach which works by assuming that highly relevant labels share some common instances, and the underlying class means of bags for each label are with a large margin. Experiments validate the effectiveness of MIMLwel in handling the weak label problem.