Multimedia Tools and Applications

, Volume 76, Issue 3, pp 4553–4570 | Cite as

Sparse multiple instance learning as document classification

  • Shengye Yan
  • Xiaodong Zhu
  • Guoqing Liu
  • Jianxin Wu
Article

Abstract

This work focuses on multiple instance learning (MIL) with sparse positive bags (which we name as sparse MIL). A structural representation is presented to encode both instances and bags. This representation leads to a non-i.i.d. MIL algorithm, miStruct, which uses a structural similarity to compare bags. Furthermore, MIL with this representation is shown to be equivalent to a document classification problem. Document classification also suffers from the fact that only few paragraphs/words are useful in revealing the category of a document. By using the TF-IDF representation which has excellent empirical performance in document classification, the miDoc method is proposed. The proposed methods achieve significantly higher accuracies and AUC (area under the ROC curve) than the state-of-the-art in a large number of sparse MIL problems, and the document classification analogy explains their efficacy in sparse MIL problems.

Keywords

Sparse multiple instance learning Low witness rate Structural representation Document classification 

References

  1. 1.
    Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: The 15th advances in neural information processing systemsGoogle Scholar
  2. 2.
    Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learningGoogle Scholar
  3. 3.
    Bunescu RC, Mooney RJ (2007) Multiple instance learning for sparse positive bags. In: The 24th international conference on machine learningGoogle Scholar
  4. 4.
    Chen Y, Bi J, Wang JZ (2006) MILES: multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931–1947CrossRefGoogle Scholar
  5. 5.
    Chen Y, Wang JZ (2004) Image categorization by learning and reasoning with regions. J Mach Learn Res 5:913–939MathSciNetGoogle Scholar
  6. 6.
    Cheung P-M, Kwok JT (2006) A regularization framework for multiple-instance learning. In: The 23 rd international conference on machine learningGoogle Scholar
  7. 7.
    Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 89:31–71CrossRefMATHGoogle Scholar
  8. 8.
    Fung G, Dundar M, Krishnappuram B, Rao RB (2007) Multiple instance learning for computer aided diagnosis. In: The 19th advances in neural information processing systemsGoogle Scholar
  9. 9.
    Gärtner T, Flach PA, Kowalczyk A, Smola AJ (2002) Multi-instance kernels. In: The 19th international conference on machine learningGoogle Scholar
  10. 10.
    Gehler PV, Chapelle O (2007) Deterministic annealing for multiple-instance learning. In: The 11th international conference on artificial intelligence and statisticsGoogle Scholar
  11. 11.
    Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97:273–324CrossRefMATHGoogle Scholar
  12. 12.
    Koller D, Friedman N (2009) Probabilistic graphical models. MIT PressGoogle Scholar
  13. 13.
    Li F, Sminchisescu C (2010) Convex multiple-instance learning by estimating likelihood ratio. In: The 24th advances in neural information processing systemsGoogle Scholar
  14. 14.
    Li W, Yeung D (2010) Mild: multiple-instance learning via disambiguation. IEEE Trans Knowl Data Eng 22(1):76–89CrossRefGoogle Scholar
  15. 15.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University PressGoogle Scholar
  16. 16.
    Rastegari M, Hajishirzi H, Farhadi A (2015) Discriminative and consistent similarities in instance-level multiple instance learningGoogle Scholar
  17. 17.
    Ray S, Craven M (2005) Supervised versus multiple instance learning: an empirical comparison. In: The 22th international conference on machine learningGoogle Scholar
  18. 18.
    Robertson S (2004) Understanding inverse document frequency: on theoretical arguments for IDF. J Doc 60:503–520CrossRefGoogle Scholar
  19. 19.
    Settles B, Craven M, Ray S (2008) Multiple instance active learning. In: The 20th advances in neural information processing systemsGoogle Scholar
  20. 20.
    Viola P, Platt J-C, Zhang C (2007) Multiple instance boosting for object detectionGoogle Scholar
  21. 21.
    Viola P, Platt JC, Zhang C (2006) Multiple instance boosting for object detection. In: The 18th advances in neural information processing systemsGoogle Scholar
  22. 22.
    Winn J, Criminisi A, Minka T (2005) Object categorization by learned universal visual dictionary. In: The 10th IEEE international conference on computer visionGoogle Scholar
  23. 23.
    Wu J (2011) Balance support vector machines locally using the structural similarity kernel. In: The 15th pacific-asia conference on knowledge discovery and data miningGoogle Scholar
  24. 24.
    Wu J, Rehg JM (2011) CENTRIST: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell. To appearGoogle Scholar
  25. 25.
    Zhang B-C, Li Z-G, Liu J (2015) A compressed sensing ensemble classifier with application to human detection. Neurocomputing 170:221–227CrossRefGoogle Scholar
  26. 26.
    Zhang B-C, Li Z-G, Perina A, Bue A-D, Murino V (2015) Adaptive local movement modelling for object tracking. In: IEEE Winter conference on applications of computer vision, pp 25–32Google Scholar
  27. 27.
    Zhang B-C, Perina A, Bue VMA-D (2015) Sparse representation classification with manifold constraints transfer. In: The IEEE conference on computer vision and pattern recognitionGoogle Scholar
  28. 28.
    Zhang B-C, Perina A, Li Z-G, Murino V, Liu J-Z, Ji R-R (2016) Bounding multiple gaussians uncertainty with application to object trackingGoogle Scholar
  29. 29.
    Zhang M, Zhou Z (2009) Multi-instance clustering with applications to multi-instance prediction. Appl Intell 31(1):47–68CrossRefGoogle Scholar
  30. 30.
    Zhang Q, Goldman S (2002) EM-DD: An improved multiple-instance learning technique. In: The 14th advances in neural information processing systemsGoogle Scholar
  31. 31.
    Zhou Z-H, Sun Y-Y, Li Y-F (2009) Multi-instance learning by treating instances as non-i.i.d. samples. In: The 26th international conference on machine learningGoogle Scholar
  32. 32.
    Zhou Z-H, Xu J-M (2007) On the relation between multi-instance learning and semi-supervised learning. In: The 24th international conference on machine learningGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shengye Yan
    • 1
  • Xiaodong Zhu
    • 1
  • Guoqing Liu
    • 2
  • Jianxin Wu
    • 3
  1. 1.B-DAT, CICAEET, School of Information and ControlNUISTNanjingChina
  2. 2.Minieye, Youjia Innovation LLCShenzhenChina
  3. 3.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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