Sparse-MIML: A sparsity-based multi-instance multi-learning algorithm

Chenyang Shen, Liping Jing, Michael K. Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Multi-Instance Multi-Label (MIML) learning is one of challenging research problems in machine learning. The main aim of this paper is to propose and develop a novel sparsity-based MIML learning algorithm. Our idea is to formulate and construct a transductive objective function for labels indicator to be learned by using the method of random walk with restart that exploits the relationships among instances and labels of objects, and computes the affinities among the objects. Then sparsity can be introduced in the labels indicator of the objective function such that relevant and irrelevant objects with respect to a given class can be distinguished. The resulting sparsity-based MIML model can be given as a constrained convex optimization problem, and it can be solved very efficiently by using the augmented Lagrangian method. Experimental results on benchmark data have shown that the proposed sparse-MIML algorithm is computationally efficient, and effective in label prediction for MIML data. We demonstrate that the performance of the proposed method is better than the other testing MIML learning algorithms.

Original languageEnglish (US)
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 9th International Conference, EMMCVPR 2013, Proceedings
Pages294-306
Number of pages13
DOIs
Publication statusPublished - Oct 8 2013
Externally publishedYes
Event9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013 - Lund, Sweden
Duration: Aug 19 2013Aug 21 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013
CountrySweden
CityLund
Period8/19/138/21/13

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Keywords

  • iterative methods
  • label ranking
  • Markov chain
  • multi-instance multi-label data
  • Sparsity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shen, C., Jing, L., & Ng, M. K. (2013). Sparse-MIML: A sparsity-based multi-instance multi-learning algorithm. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 9th International Conference, EMMCVPR 2013, Proceedings (pp. 294-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8081 LNCS). https://doi.org/10.1007/978-3-642-40395-8_22