A NEW ADAPTIVE CLASSIFICATION SCHEME BASED ON SKELETON INFORMATION

Catalina Cocianu, Luminita State, Ion Roşca, Panayiotis Vlamos

2007

Abstract

Large multivariate data sets can prove difficult to comprehend, and hardly allow the observer to figure out the pattern structures, relationships and trends existing in samples and justifies the efforts of finding suitable methods from extracting relevant information from data. In our approach, we consider a probabilistic class model where each class h ∈ H is represented by a probability density function defined on R n ; where n is the dimension of input data and H stands for a given finite set of classes. The classes are learned by the algorithm using the information contained by samples randomly generated from them. The learning process is based on the set of class skeletons, where the class skeleton is represented by the principal axes estimated from data. Basically, for each new sample, the recognition algorithm classifies it in the class whose skeleton is the “nearest” to this example. For each new sample allotted to a class, the class characteristics are re-computed using a first order approximation technique. Experimentally derived conclusions concerning the performance of the new proposed method are reported in the final section of the paper.

References

  1. Cortes, C., Vapnik, V., 1995. Support Vector networks. In Machine Learning 20: 273-297
  2. Diamantaras, K.I., Kung, S.Y., 1996. Principal Component Neural Networks: theory and applications, John Wiley &Sons
  3. Everitt, B. S., 1978. Graphical Techniques for Multivariate Data, North Holland, NY
  4. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., 1996. Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Menlo Park, CA.
  5. Frieß, T., Cristianini, N., and Campbell, C., 1998. The kernel adatron algorithm: A fast and simple learning procedure for support vector machines. In 15th Intl. Conf. Machine Learning. Morgan Kaufmann Publishers
  6. Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R., 2004. Neighbourhood Component Analysis. In Proceedings of the Conference on Advances in Neural Information Processing Systems
  7. Gordon, A.D. 1999. Classification, Chapman&Hall/CRC, 2nd Edition
  8. Hastie, T., Tibshirani, R., Friedman, J. 2001. The Elements of Statistical Learning Data Mining, Inference, and Prediction. Springer-Verlag
  9. Hyvarinen, A., Karhunen, J., Oja, E., 2001. Independent Component Analysis, John Wiley &Sons
  10. Jain,A.K., Dubes,R., 1988. Algorithms for Clustering Data, Prentice Hall,Englewood Cliffs, NJ.
  11. Jain, A.K., Murty, M.N., Flynn, P.J. 1999. Data clustering: a review. ACM Computing Surveys, Vol. 31, No. 3, September 1999
  12. Krishnapuram, R., Keller,J.M., 1993. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst., 1(2)
  13. Liu, J., and Chen, S. 2006. Discriminant common vectors versus neighbourhood components analysis and Laplacianfaces: A comparative study in small sample size problem. Image and Vision Computing 24 (2006) 249-262
  14. Panayirci,E., Dubes,R.C., 1983. A test for multidimensional clustering tendency. Pattern Recognition,16, 433-444
  15. Smith,S.P., Jain,A.K., 1984. Testing for uniformity in multidimensional data, In IEEE Trans.Patt. Anal. and Machine Intell., 6(1),73-81
  16. State, L., Cocianu, C., Vlamos, P, Stefanescu, V., 2006. PCA-Based Data Mining Probabilistic and Fuzzy Approaches with Applications in Pattern Recognition. In Proceedings of ICSOFT 2006, Portugal, pp. 55-60.
  17. Ripley, B.D. 1996. Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.
Download


Paper Citation


in Harvard Style

Cocianu C., State L., Roşca I. and Vlamos P. (2007). A NEW ADAPTIVE CLASSIFICATION SCHEME BASED ON SKELETON INFORMATION . In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007) ISBN 978-989-8111-13-5, pages 85-92. DOI: 10.5220/0002137900850092


in Bibtex Style

@conference{sigmap07,
author={Catalina Cocianu and Luminita State and Ion Roşca and Panayiotis Vlamos},
title={A NEW ADAPTIVE CLASSIFICATION SCHEME BASED ON SKELETON INFORMATION},
booktitle={Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)},
year={2007},
pages={85-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002137900850092},
isbn={978-989-8111-13-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)
TI - A NEW ADAPTIVE CLASSIFICATION SCHEME BASED ON SKELETON INFORMATION
SN - 978-989-8111-13-5
AU - Cocianu C.
AU - State L.
AU - Roşca I.
AU - Vlamos P.
PY - 2007
SP - 85
EP - 92
DO - 10.5220/0002137900850092