|
Published Articles >> Table of Contents >> Abstract
16th International Conference on Pattern Recognition (ICPR'02) - Volume 2
p. 20052
Classification of Binary Vectors by Using ΔSC-Distance
Pasi Franti, University of Joensuu
Mantao Xu, University of Joensuu
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.1048234
Send link to a friend
| Abstract |
|
Stochastic complexity (SC) has been employed as a cost function for solving binary clustering problem Shannon code length (CL-distance) has been previously applied for the purpose of classifying the data vectors during the clustering process. The CL-distance, however, is defined for a given (static) clustering only, and it does not take into account of the changes in the class distribution during the clustering process. We propose a new ΔSC-distance function based on a design paradigm, in which the distance function is derived directly from the difference of the cost function value before and after the classification. The ΔSC is general in the sense that it does not depend on the algorithm in which it is applied. The effect of the new distance function is demonstrated by implementing it with the GLA and the RLS clustering algorithms.
|
 Additional Information
|
Citation:
Pasi Franti, Mantao Xu,
"Classification of Binary Vectors by Using ΔSC-Distance,"
icpr,
p. 20052,
16th International Conference on Pattern Recognition (ICPR'02) - Volume 2,
2002
|
|
