Knowledge and Information Systems

, Volume 42, Issue 2, pp 381–407 | Cite as

MILDE: multiple instance learning by discriminative embedding

Regular Paper
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Abstract

While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.

Keywords

Multi-instance learning Codebook Bag of words 
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Notes

Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This work was supported by the fellowship RYC-2008-03789 and the Spanish project TRA2011-29454-C03-01.

References

  1. 1.
    Dietterich TG, Lathrop RH, Lozano-Perez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 89:31–71CrossRefMATHGoogle Scholar
  2. 2.
    Kotsiantis S, Kanellopoulos D (2008) Multi-instance learning for bankruptcy prediction. In: Proceedings of international conference on convergence and hybrid information technology, pp 1007–1012Google Scholar
  3. 3.
    Chen Y, Bi J, Wang JZ (2006) MILES: multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28:1931–1947CrossRefGoogle Scholar
  4. 4.
    Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted Gaussian mixture models. Dig Signal Process 10:19–41CrossRefGoogle Scholar
  5. 5.
    Oded M (1998) Learning from ambiguity. PhD thesis, MITGoogle Scholar
  6. 6.
    Zhang Q, Goldman SA (2001) EM-DD: an improved multiple-instance learning technique. In: Proceedings of neural information processing systems, pp 1073–1080Google Scholar
  7. 7.
    Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Proceedings of neural information processing systems, pp 561–568Google Scholar
  8. 8.
    Xu X, Frank E (2004) Logistic regression and boosting for labeled bags of instances. In: Proceedings of Pacific Asia conference on knowledge discovery and data mining, pp 272–281Google Scholar
  9. 9.
    Gehler PV, Chapelle O (2007) Deterministic annealing for multiple-instance learning. In: Proceedings of international conference on artificial intelligence and statistics, pp 123–130Google Scholar
  10. 10.
    Antic B, Ommer B (2013) Robust multiple-instance learning with superbags. Lecture notes in computer science, vol 7725, pp 242–255Google Scholar
  11. 11.
    Scott S, Zhang J, Brown J (2005) On generalized multiple-instance learning. Int J Comput Intell Appl 5:21–35CrossRefGoogle Scholar
  12. 12.
    Zhou Z-H, Zhang M-L (2007) Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowl Inf Syst 11:155–170CrossRefGoogle Scholar
  13. 13.
    Gärtner T, Flach PA, Kowalczyk A, Smola AJ (2002) Multi-instance kernels. In: Proceedings of international conference on machine learning, pp 179–186Google Scholar
  14. 14.
    Foulds J, Frank E (2010) A review of multi-instance learning assumptions. Knowl Eng Rev 25:1–25CrossRefGoogle Scholar
  15. 15.
    Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. In: Proceedings of European conference on computer vision, pp 490–503Google Scholar
  16. 16.
    Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Gool LV (2005) A comparison of affine region detectors. Int J Comput Vis 65:43–72CrossRefGoogle Scholar
  17. 17.
    Maron O, Lozano-Pérez T (1998) A framework for multiple-instance learning. In: Proceedings of neural information processing systems, pp 570–576Google Scholar
  18. 18.
    Bunescu R, Mooney R (2007) Multiple instance learning for sparse positive bags. In: Proceedings of international conference on machine learning, pp 105–112Google Scholar
  19. 19.
    Zhou Z-H, Sun Y-Y, Li Y-F (2009), Multi-instance learning by treating instances as non i.i.d. samples. In: Proceedings of international conference on machine learning, pp 1249–1256Google Scholar
  20. 20.
    Zhang M-L, Zhou Z-H (2009) Multi-instance clustering with applications to multi-instance prediction. Appl Intell 31:47–68CrossRefGoogle Scholar
  21. 21.
    Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874MATHGoogle Scholar
  22. 22.
    Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. Technical report, National Taiwan UniversityGoogle Scholar
  23. 23.
    Yang J (2005) Mill: a multi-instance learning library. http://www-2.cs.cmu.edu/juny/IR-lab/

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Computer Vision CenterUniverstitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

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