Advertisement

An Effective Permutant Selection Heuristic for Proximity Searching in Metric Spaces

  • Karina Figueroa
  • Rodrigo Paredes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)

Abstract

The permutation based index has shown to be very effective in medium and high dimensional metric spaces, even in difficult problems such as solving reverse k-nearest neighbor queries. Nevertheless, currently there is no study about which are the desirable features one can ask to a permutant set, or how to select good permutants. Similar to the case of pivots, our experimental results show that, compared with a randomly chosen set, a good permutant set yields to fast query response or to reduce the amount of space used by the index. In this paper, we start by characterizing permutants and studying their predictive power; then we propose an effective heuristic to select a good set of permutant candidates. We also show empirical evidence that supports our technique.

References

  1. 1.
    Bustos, B., Navarro, G., Chávez, E.: Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters 24(14), 2357–2366 (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bustos, B., Pedreira, O., Brisaboa, N.R.: A dynamic pivot selection technique for similarity search. In: Proc. 1st Workshop on Similarity Search and Applications (SISAP 2008), pp. 105–112 (2008)Google Scholar
  3. 3.
    Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 30(9), 1647–1658 (2009)Google Scholar
  4. 4.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquin, J.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  5. 5.
    Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Falchi, F., Kacimi, M., Mass, Y., Rabitti, F., Zezula, P.: SAPIR: Scalable and distributed image searching. In: SAMT (Posters and Demos). CEUR Workshop Proceedings, vol. 300, pp. 11–12 (2007)Google Scholar
  7. 7.
    Hjaltason, G., Samet, H.: Index-driven similarity search in metric spaces. ACM Transactions Database Systems 28(4), 517–580 (2003)CrossRefGoogle Scholar
  8. 8.
    Micó, L., Oncina, J., Vidal, E.: A new version of the nearest-neighbor approximating and eliminating search (AESA) with linear preprocessing-time and memory requirements. Pattern Recognition Letters 15, 9–17 (1994)CrossRefGoogle Scholar
  9. 9.
    Pedreira, O., Brisaboa, N.R.: Spatial selection of sparse pivots for similarity search in metric spaces. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 434–445. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann (2006)Google Scholar
  11. 11.
    Yianilos, P.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proc. 4th ACM-SIAM Symposium on Discrete Algorithms (SODA 1993), pp. 311–321 (1993)Google Scholar
  12. 12.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search – The Metric Space Approach. Advances in Database System, vol. 32. Springer (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karina Figueroa
    • 1
  • Rodrigo Paredes
    • 2
  1. 1.Facultad de Ciencias Físico-MatemáticasUniversidad MichoacanaMéxico
  2. 2.Departamento de Ciencias de la ComputaciónUniversidad de TalcaChile

Personalised recommendations