MILEAGE: Multiple Instance LEArning with Global Embedding

Dan Zhang, Jingrui He, Luo Si, Richard D. Lawrence

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

8 Scopus citations

Abstract

Multiple Instance Learning (MIL) generally represents each example as a collection of instances such that the features for local objects can be better captured, whereas traditional methods typically extract a global feature vector for each example as an integral part. However, there is limited research work on investigating which of the two learning scenarios performs better. This paper proposes a novel framework - Multiple Instance LEArning with Global Embedding (MILEAGE), in which the global feature vectors for traditional learning methods are integrated into the MIL setting. Within the proposed framework, a large margin method is formulated to adaptively tune the weights on the two different kinds of feature representations (i.e., global and multiple instance) for each example and trains the classifier simultaneously. An extensive set of experiments are conducted to demonstrate the advantages of the proposed method.

Original languageEnglish (US)
Title of host publication30th International Conference on Machine Learning, ICML 2013
PublisherInternational Machine Learning Society (IMLS)
Pages1119-1127
Number of pages9
EditionPART 2
StatePublished - 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Other

Other30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period6/16/136/21/13

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

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  • Cite this

    Zhang, D., He, J., Si, L., & Lawrence, R. D. (2013). MILEAGE: Multiple Instance LEArning with Global Embedding. In 30th International Conference on Machine Learning, ICML 2013 (PART 2 ed., pp. 1119-1127). International Machine Learning Society (IMLS).