Multi-instance multi-graph dual embedding learning

Jia Wu, Xingquan Zhu, Chengqi Zhang, Zhihua Cai

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

24 Citations (Scopus)

Abstract

Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented by a set of well defined feature values. In this paper, we advance the problem to a multi-instance multi-graph setting, where a bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. To achieve the goal, the main challenge is to properly represent graphs inside each bag and further take advantage of complementary information between instance and graph pairs for learning. In the paper, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative sub graphs discovery process, and (2) embed discovered sub graphs into the instance feature selection process. The dual embedding process results in an optimal representation for each bag to provide combined instance and graph information for learning. Experiments and comparisons on real-world multi-instance multi-graph learning tasks demonstrate the algorithm performance.
Original languageEnglish
Title of host publication2013 IEEE 13th International Conference on Data Mining ICDM2013
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages827-836
Number of pages10
ISBN (Electronic)9780769551081
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, United States
Duration: 7 Dec 201310 Dec 2013

Conference

Conference13th IEEE International Conference on Data Mining, ICDM 2013
CountryUnited States
CityDallas
Period7/12/1310/12/13

Keywords

  • data mining
  • feature selection
  • graph theory
  • learning (artificial intelligence)
  • pattern classification
  • labeled instance bags
  • multiinstance multigraph setting
  • classification models
  • dual embedding multiinstance multigraph learning algorithm
  • DE-MIMG algorithm
  • dual embedding learning approach
  • informative subgraph discovery process
  • instance feature selection process
  • dual embedding process
  • graph information
  • Bismuth
  • Vectors
  • Linear programming
  • Educational institutions
  • Kernel
  • Laplace equations
  • Computer science
  • Classification
  • Multi-instance
  • Multi-graph
  • Graph
  • Embedding

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