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Neural Computing and Applications

, Volume 28, Supplement 1, pp 983–992 | Cite as

Domain transfer multi-instance dictionary learning

  • Ke Wang
  • Jiayong Liu
  • Daniel González
Original Article
  • 413 Downloads

Abstract

In this paper, we invest the domain transfer learning problem with multi-instance data. We assume we already have a well-trained multi-instance dictionary and its corresponding classifier from the source domain, which can be used to represent and classify the bags. But it cannot be directly used to the target domain. Thus we propose to adapt them to the target domain by adding an adaptive term to the source domain classifier. The adaptive function is a linear function based on a domain transfer multi-instance dictionary. Given a target domain bag, we first map it to a bag-level feature space using the domain transfer dictionary and then apply a linear adaptive function to its bag-level feature vector. To learn the domain transfer dictionary and the adaptive function parameter, we simultaneously minimize the average classification error of the target domain classifier over the target domain training set, and the complexities of both the adaptive function parameter and the domain transfer dictionary. The minimization problem is solved by an iterative algorithm which updates the dictionary and the function parameter alternately. Experiments over several benchmark data sets show the advantage of the proposed method over existing state-of-the-art domain transfer multi-instance learning methods.

Keywords

Multi-instance learning Domain transfer learning Classifier adaptation Gradient descent 

References

  1. 1.
    Awad G, Over P, Kraaij W (2014) Content-based video copy detection benchmarking at trecvid. ACM Trans Inf Syst 32(3):14CrossRefGoogle Scholar
  2. 2.
    Chen A, Eberle M, Lunt E, Liu S, Leake K, Rudenko M, Hawkins A, Schmidt H (2011) Dual-color fluorescence cross-correlation spectroscopy on a planar optofluidic chip. Lab Chip 11(8):1502–1506CrossRefGoogle 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(12):1931–1947CrossRefGoogle Scholar
  4. 4.
    Ding Z, Shao M, Fu Y (2015) Deep low-rank coding for transfer learning. In: Proceedings of the 24th international conference on artificial intelligence. AAAI Press, pp 3453–3459Google Scholar
  5. 5.
    Duan L, Tsang IW, Xu D (2012) Domain transfer multiple kernel learning. IEEE Trans Pattern Anal Mach Intell 34(3):465–479CrossRefGoogle Scholar
  6. 6.
    Duan L, Tsang IW, Xu D, Maybank SJ (2009) Domain transfer svm for video concept detection. In: IEEE Conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 1375–1381. IEEEGoogle Scholar
  7. 7.
    Fan X, Malone B, Yuan C (2014) Finding optimal Bayesian network structures with constraints learned from data. In: Proceedings of the 30th annual conference on uncertainty in artificial intelligence (UAI-14), pp 200–209Google Scholar
  8. 8.
    Fan X, Tang K (2010) Enhanced maximum auc linear classifier. In: 2010 Seventh international conference on fuzzy systems and knowledge discovery (FSKD), vol 4, pp. 1540–1544. IEEEGoogle Scholar
  9. 9.
    Fan X, Tang K, Weise T (2011) Margin-based over-sampling method for learning from imbalanced datasets. In: Advances in knowledge discovery and data mining. Springer, pp 309–320Google Scholar
  10. 10.
    Fan X, Yuan C (2015) An improved lower bound for bayesian network structure learning. In: Twenty-ninth AAAI conference on artificial intelligence, pp 2439 – 2445Google Scholar
  11. 11.
    Fan X, Yuan C, Malone B (2014) Tightening bounds for bayesian network structure learning. In: Proceedings of the 28th AAAI conference on artificial intelligence, pp 2439 – 2445Google Scholar
  12. 12.
    Fu Z, Robles-Kelly A, Zhou J (2011) Milis: multiple instance learning with instance selection. IEEE Trans Pattern Anal Mach Intell 33(5):958–977CrossRefGoogle Scholar
  13. 13.
    Hammami N, Bedda M (2010) Improved tree model for arabic speech recognition. In: 2010 3rd IEEE international conference on computer science and information technology (ICCSIT), vol 5. IEEE, pp 521–526Google Scholar
  14. 14.
    Hammami N, Sellam M (2009) Tree distribution classifier for automatic spoken arabic digit recognition. In: International conference for internet technology and secured transactions, 2009, ICITST 2009. IEEE, pp 1–4Google Scholar
  15. 15.
    Lin F, Wang J, Zhang N, Xiahou J, McDonald N (2016) Multi-kernel learning for multivariate performance measures optimization. Neural Comput Appl 1–13Google Scholar
  16. 16.
    Ling X, Dai W, Xue GR, Yang Q, Yu Y (2008) Spectral domain-transfer learning. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 488–496Google Scholar
  17. 17.
    Liu S, Hawkins AR, Schmidt H (2016) Optofluidic devices with integrated solid-state nanopores. Microchim Acta 183(4):1275–1287CrossRefGoogle Scholar
  18. 18.
    Liu S, Wall TA, Ozcelik D, Parks JW, Hawkins AR, Schmidt H (2015) Electro-optical detection of single \( \lambda \)-dna. Chem Commun 51(11):2084–2087CrossRefGoogle Scholar
  19. 19.
    Liu S, Yuzvinsky TD, Schmidt H (2013) Effect of fabrication-dependent shape and composition of solid-state nanopores on single nanoparticle detection. ACS Nano 7(6):5621–5627CrossRefGoogle Scholar
  20. 20.
    Liu S, Zhao Y, Parks JW, Deamer DW, Hawkins AR, Schmidt H (2014) Correlated electrical and optical analysis of single nanoparticles and biomolecules on a nanopore-gated optofluidic chip. Nano Lett 14(8):4816–4820CrossRefGoogle Scholar
  21. 21.
    Liu X, Wang J, Yin M, Edwards B, Xu P (2015) Supervised learning of sparse context reconstruction coefficients for data representation and classification. Neural Comput Appl 1–9Google Scholar
  22. 22.
    Liu Y, Yang J, Zhou Y, Hu J (2013) Structure design of vascular stents. Multiscale Simul Mech Biol Mater 301–317Google Scholar
  23. 23.
    Long M, Ding G, Wang J, Sun J, Guo Y, Yu P (2013) Transfer sparse coding for robust image representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 407–414Google Scholar
  24. 24.
    Lu G, Yan Y, Ren L, Saponaro P, Sebe N, Kambhamettu C (2016) Where am i in the dark: exploring active transfer learning on the use of indoor localization based on thermal imaging. Neurocomputing 173:83–92CrossRefGoogle Scholar
  25. 25.
    Mei S, Zhu H (2015) Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways. BMC Bioinf 16(1):417CrossRefGoogle Scholar
  26. 26.
    Oomen J, Over P, Kraaij W, Smeaton A (2013) Symbiosis between the trecvid benchmark and video libraries at the netherlands institute for sound and vision. Int J Digit Libr 13(2):91–104CrossRefGoogle Scholar
  27. 27.
    Pan SJ, Ni X, Sun JT, Yang Q, Chen Z (2010) Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th international conference on World wide web. ACM, pp 751–760Google Scholar
  28. 28.
    Peng B, Liu Y, Zhou Y, Yang L, Zhang G, Liu Y (2015) Modeling nanoparticle targeting to a vascular surface in shear flow through diffusive particle dynamics. Nanoscale Res Lett 10(1):1–9CrossRefGoogle Scholar
  29. 29.
    Redko I, Bennani Y (2016) Non-negative embedding for fully unsupervised domain adaptation. Pattern Recogn Lett 77:35–41CrossRefGoogle Scholar
  30. 30.
    Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI 2014), pp 853–858Google Scholar
  31. 31.
    Wang J, Wang H, Zhou Y, McDonald N (2015) Multiple kernel multivariate performance learning using cutting plane algorithm. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC), pp 1870–1875Google Scholar
  32. 32.
    Wang J, Zhou Y, Duan K, Wang JJY, Bensmail H (2015) Supervised cross-modal factor analysis for multiple modal data classification. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC), pp 1882–1888Google Scholar
  33. 33.
    Wang JJY, Bensmail H (2013) Cross-domain sparse coding. In: CIKM 2013, proceedings of the 22nd ACM international conference on Conference on information and knowledge management. ACM, pp 1461–1464Google Scholar
  34. 34.
    Wang JJY, Sun Y, Bensmail H (2014) Domain transfer nonnegative matrix factorization. In: 2014 International joint conference on neural networks (IJCNN). IEEE, pp 3605–3612Google Scholar
  35. 35.
    Wang Q, Ruan L, Si L (2014) Adaptive knowledge transfer for multiple instance learning in image classification. In: AAAI conference on artificial intelligenceGoogle Scholar
  36. 36.
    Wang S, Zhou Y, Tan J, Xu J, Yang J, Liu Y (2014) Computational modeling of magnetic nanoparticle targeting to stent surface under high gradient field. Comput Mech 53(3):403–412CrossRefMATHGoogle Scholar
  37. 37.
    Xu J, Yang J, Huang N, Uhl C, Zhou Y, Liu Y (2016) Mechanical response of cardiovascular stents under vascular dynamic bending. Biomed Eng Online 15(1):1CrossRefGoogle Scholar
  38. 38.
    Yang J, Yan R, Hauptmann AG (2007) Cross-domain video concept detection using adaptive svms. In: Proceedings of the 15th international conference on multimedia. ACM, pp 188–197Google Scholar
  39. 39.
    Yang L, Jing L, Ng M (2015) Robust and non-negative collective matrix factorization for text-to-image transfer learning. IEEE Trans Image Process 24(12):4701–4714MathSciNetCrossRefGoogle Scholar
  40. 40.
    Zhang D, Si L (2009) Multiple instance transfer learning. In: IEEE international conference on data mining workshops, 2009. ICDMW’09. IEEE, pp 406–411Google Scholar
  41. 41.
    Zhao S, Cao Q, Chen J, Zhang Y, Tang J, Duan Z (2016) A multi-atl method for transfer learning across multiple domains with arbitrarily different distribution. Knowl Based Syst 94:60–69CrossRefGoogle Scholar
  42. 42.
    Zhou Y, Hu W, Peng B, Liu Y (2014) Biomarker binding on an antibody-functionalized biosensor surface: the influence of surface properties, electric field, and coating density. J Phys Chem C 118(26):14586–14594CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.College of MathematicsSichuan UniversityChengduChina
  2. 2.College of Electronics and Information EngineeringSichuan UniversityChengduChina
  3. 3.Computer Science DepartmentCatholic University of MurciaMurciaSpain

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