# PCA Supervised and Unsupervised Classifiers in Signal Processing

### Catalina Cocianu, Luminita State, Panayiotis Vlamos, Constantin Doru, Corina Sararu

#### Abstract

The aims of the research reported in this paper are to investigate the potential of principal directions-based approach in supervised and unsupervised frameworks. The structure of a class is represented in terms of the estimates of its principal directions computed from data, the overall dissimilarity of a particular object with a given class being given by the “disturbance” of the structure, when the object is identified as a member of this class. In case of unsupervised framework, the clusters are computed using the estimates of the principal directions. Our attempt uses arguments based on the principal components to refine the basic idea of k-means aiming to assure soundness and homogeneity to the resulted clusters. Each cluster is represented in terms of its skeleton given by a set of orthogonal and unit eigen vectors (principal directions) of sample covariance matrix, a set of principal directions corresponding to the maximum variability of the “cloud” from metric point of view. A series of conclusions experimentally established are exposed in the final section of the paper.

#### References

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#### Paper Citation

#### in Harvard Style

Cocianu C., State L., Vlamos P., Doru C. and Sararu C. (2009). **PCA Supervised and Unsupervised Classifiers in Signal Processing** . In *Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009)* ISBN 978-989-8111-89-0, pages 61-70. DOI: 10.5220/0002195000610070

#### in Bibtex Style

@conference{pris09,

author={Catalina Cocianu and Luminita State and Panayiotis Vlamos and Constantin Doru and Corina Sararu},

title={PCA Supervised and Unsupervised Classifiers in Signal Processing},

booktitle={Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009)},

year={2009},

pages={61-70},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0002195000610070},

isbn={978-989-8111-89-0},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009)

TI - PCA Supervised and Unsupervised Classifiers in Signal Processing

SN - 978-989-8111-89-0

AU - Cocianu C.

AU - State L.

AU - Vlamos P.

AU - Doru C.

AU - Sararu C.

PY - 2009

SP - 61

EP - 70

DO - 10.5220/0002195000610070