A NEW LEARNING ALGORITHM FOR CLASSIFICATION IN THE REDUCED SPACE

Luminita State, Catalina Cocianu, Ion Rosca, Panayiotis Vlamos

Abstract

The aim of the research reported in the paper was twofold: to propose a new approach in cluster analysis and to investigate its performance, when it is combined with dimensionality reduction schemes. Our attempt is based on group skeletons defined by a set of orthogonal and unitary eigen vectors (principal directions) of the sample covariance matrix. Our developments impose a set of quite natural working assumptions on the true but unknown nature of the class system. The search process for the optimal clusters approximating the unknown classes towards getting homogenous groups, where the homogeneity is defined in terms of the “typicality” of components with respect to the current skeleton. Our method is described in the third section of the paper. The compression scheme was set in terms of the principal directions corresponding to the available cloud. The final section presents the results of the tests aiming the comparison between the performances of our method and the standard k-means clustering technique when they are applied to the initial space as well as to compressed data.

References

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Paper Citation


in Harvard Style

State L., Cocianu C., Rosca I. and Vlamos P. (2008). A NEW LEARNING ALGORITHM FOR CLASSIFICATION IN THE REDUCED SPACE . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 155-160. DOI: 10.5220/0001676501550160


in Bibtex Style

@conference{iceis08,
author={Luminita State and Catalina Cocianu and Ion Rosca and Panayiotis Vlamos},
title={A NEW LEARNING ALGORITHM FOR CLASSIFICATION IN THE REDUCED SPACE},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={155-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001676501550160},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A NEW LEARNING ALGORITHM FOR CLASSIFICATION IN THE REDUCED SPACE
SN - 978-989-8111-37-1
AU - State L.
AU - Cocianu C.
AU - Rosca I.
AU - Vlamos P.
PY - 2008
SP - 155
EP - 160
DO - 10.5220/0001676501550160