Skip to main content

Abstract

Similarity measures are very much essential in solving many data mining tasks such as clustering, information retrieval, and classification. A large number of the similarity measures directly or indirectly depend upon distance. Recently developed mass-based similarity measure, Massim, is well established in information retrieval task with algorithm MassIR. This paper will examine the probable uses of mass-based similarity measure in classification tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jacobs, D.W., Weinshall, D., Gdalyahu, Y.: Classification with non-metric distances: image retrieval and class representation. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 583–600 (2000)

    Article  Google Scholar 

  2. Pekalska, E., Paclíc, P., Duin, R.P.W.: A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res. 2, 175–211 (2001)

    MathSciNet  Google Scholar 

  3. Hochreiter, S., Obermayer, K.: Support vector machines for dyadic data. Neural Comput. 18(6), 1472–1510 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Simard, P., Cun, Y.L., Denker, J.: Efficient pattern recognition using a new transformation distance. Adv. Neural Inf. Process. Syst. 5, 50–68 (1993)

    Google Scholar 

  5. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  6. Ting, K.M., Fernando, T.L., Webb, G.I.: Mass-Based Similarity Measure: An Effective Alternative to Distance-Based Similarity Measures, Technical report 2013/276, Calyton School of IT, Monash University, Australia (2013)

    Google Scholar 

  7. Skopal, T., Bustos, B.: On nonmetric similarity search problems in complex domains. ACM Comput. Surv. (CSUR) 43(4), 34 (2011)

    Article  MATH  Google Scholar 

  8. Choi, S.S., Cha, S.H., Tappert, C.C.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inf. 8(1) (2010)

    Google Scholar 

  9. Ting, K.M., Zhou, G.T., Liu, F.T., Tan, J.S.C.: Mass estimation and its applications. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 989−998 (2010)

    Google Scholar 

  10. Ting, K.M., Zhou, G.T., Liu, F.T., Tan, S.C.: Mass estimation. Mach. Learn. 90(1), 127–160 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871–883 (1999)

    Article  Google Scholar 

  12. Brog, I., Gronen, P.J.F.: Modern Multidimensional Scaling: Theory and Applications, 2nd edn. Springer, New York (2005)

    Google Scholar 

  13. Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Caazzanti, L.: Similarity-based classification: concepts and algorithms. J. Mach. Learn. Res. 10, 747–776 (2009)

    MathSciNet  MATH  Google Scholar 

  14. Wu, G., Chang, E.Y., Zhang, Z.: An Analysis of Transformation on Non-Positive Semi Definite Similarity Matrix for Kernel Machines. Technical report, University of California, Santa Barbara, March 2005

    Google Scholar 

  15. Schölkopf, B.: The kernel trick for distances. In: Advances in Neural Information Processing Systems, vol. 13 (2001)

    Google Scholar 

  16. Hochreiter, S., Mozer, M.C., Obermayer, K.: Coulomb classifiers: generalizing support vector machines via an analogy to electrostatic systems. In: Advances in Neural Information Processing Systems, vol. 15, pp. 545–552 (2003)

    Google Scholar 

  17. Duin, R.P.W., Pekalska, E., de Ridder, D.: Relational discriminant analysis. Pattern Recogn. Lett. 20, 1175–1181 (1999)

    Article  Google Scholar 

  18. Graepel, T., Herbrich, R., Bollmann-Sdorra, P., Obermayer, K.: Classification on pairwise proximity data. In: Advances in Neural Information Processing Systems, pp. 438−444 (1998)

    Google Scholar 

  19. Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K.-R., Obermayer, K., Williamson, R.: Classification on proximity data with LP–machines. In: Proceedings of the International Conference on Artificial Neural Networks (1999)

    Google Scholar 

  20. Liao, L., Noble, W.S.: Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J. Comput. Biol. 10(6), 857–868 (2003)

    Article  Google Scholar 

  21. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)

    Book  MATH  Google Scholar 

  22. Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)

    MATH  Google Scholar 

  23. Shalev-Shwartz, S., Singer, Y., Ng, A.Y.: Online and batch learning of pseudo-metrics. In: Proceedings of the Twenty-first International Conference on Machine learning, ACM, p. 94 (2004)

    Google Scholar 

  24. Baoli, L., Qin, L., Shiwen, Y.: An adaptive k-nearest neighbor text categorization strategy. ACM Trans. Asian Lang. Inf. Process. (TALIP) 3(4), 215–226 (2004)

    Article  Google Scholar 

  25. Qamar, A.M., Gaussier, E., Chevallet, J.P., Lim, J.H.: Similarity learning for nearest neighbor classification. In: Eighth IEEE International Conference on Data Mining, ICDM’08, pp. 983–988. IEEE (2008)

    Google Scholar 

  26. Pekalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity based classifiers. Pattern Recogn. Lett. 39, 189–208 (2006)

    Article  MATH  Google Scholar 

  27. Lozano, M., Sotoca, J.M., Sánchez, J.S., Pla, F., Pekalska, E., Duin, R.P.W.: Experimental study on prototype optimisation algorithms for prototype based classification in vector spaces. Pattern Recogn. 39, 1827–1838 (2006)

    Article  MATH  Google Scholar 

  28. Cazzanti, L., Gupta, M.R., Koppal, A.J.: Generative models for similarity-based classification. Pattern Recogn. 41(7), 2289–2297 (2008)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ashish Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Ashish Kumar, Roheet Bhatnagar, Sumit Srivastava (2016). Can We Use Mass-Based Similarity Measure in Classification?. In: Afzalpulkar, N., Srivastava, V., Singh, G., Bhatnagar, D. (eds) Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2638-3_91

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2638-3_91

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2636-9

  • Online ISBN: 978-81-322-2638-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics