loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Luminita State 1 ; Catalina Cocianu 2 ; Doru Constantin 1 ; Corina Sararu 1 and Panayiotis Vlamos 3

Affiliations: 1 University of Pitesti, Romania ; 2 Academy of Economic Studies, Romania ; 3 Ionian University, Greece

Keyword(s): Principal component analysis, Data compression/decompression, Statistical signal classification, First order approximation for eigen values and eigen vectors.

Related Ontology Subjects/Areas/Topics: Biometrics and Pattern Recognition ; Multimedia ; Multimedia Signal Processing ; Telecommunications

Abstract: Since similarity plays a key role for both clustering and classification purposes, the problem of finding a relevant indicators to measure the similarity between two patterns drawn from the same feature space became of major importance. The advantages of using principal components reside from the fact that bands are uncorrelated and no information contained in one band can be predicted by the knowledge of the other bands. The semi-supervised learning (SSL) problem has recently drawn large attention in the machine learning community, mainly due to its significant importance in practical applications. The aims of the research reported in this paper are to report experimentally derived conclusions on the performance of a PCA-based supervised technique in a semi-supervised environment. A series of conclusions experimentally established by tests performed on samples of signals coming from two classes are exposed in the final section of the paper.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.145.15.205

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
State, L.; Cocianu, C.; Constantin, D.; Sararu, C. and Vlamos, P. (2009). TOWARD A SEMI-SUPERVISED APPROACH IN CLASSIFICATION BASED ON PRINCIPAL DIRECTIONS. In Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP; ISBN 978-989-674-007-8, SciTePress, pages 68-73. DOI: 10.5220/0002233200680073

@conference{sigmap09,
author={Luminita State. and Catalina Cocianu. and Doru Constantin. and Corina Sararu. and Panayiotis Vlamos.},
title={TOWARD A SEMI-SUPERVISED APPROACH IN CLASSIFICATION BASED ON PRINCIPAL DIRECTIONS},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP},
year={2009},
pages={68-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002233200680073},
isbn={978-989-674-007-8},
}

TY - CONF

JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications (ICETE 2009) - SIGMAP
TI - TOWARD A SEMI-SUPERVISED APPROACH IN CLASSIFICATION BASED ON PRINCIPAL DIRECTIONS
SN - 978-989-674-007-8
AU - State, L.
AU - Cocianu, C.
AU - Constantin, D.
AU - Sararu, C.
AU - Vlamos, P.
PY - 2009
SP - 68
EP - 73
DO - 10.5220/0002233200680073
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic