CyberinfraV1, Main, Exploration, bibRecord, 000B65

A Simple and Effective Algorithm for Quasar Candidate Selection

Identifieur interne : 000B65 ( Main/Exploration ); précédent : 000B64; suivant : 000B66

A Simple and Effective Algorithm for Quasar Candidate Selection

Auteurs : NANBO PENG [République populaire de Chine] ; YANXIA ZHANG [République populaire de Chine] ; TONG PEI [République populaire de Chine] ; YONGHENG ZHAO [République populaire de Chine]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.

RBID : Pascal:11-0070244

Descripteurs français

Pascal (Inist)
- Cosmologie, Plus proche voisin, Classification, Image couleur, Histogramme, Haute performance, Métrique, Quasar, Etoile.
Wicri :
- topic : Cosmologie, Classification.

English descriptors

KwdEn :
- Classification, Color image, Cosmology, High performance, Histogram, Metrics, Nearest neighbour, Quasars, Stars.

Abstract

K-Nearest Neighbor (kNN) algorithm is one of the simplest and most flexible and effective classification algorithms, which has been widely used in many fields. Using the multi-band samples extracted from large surveys of SDSS DR7 and UKIDSS DR3, we investigate the performance of kNN with different combinations of colors to select quasar candidates. The color histograms of quasars and stars is helpful to select the optimal input pattern for the classifier of kNN. The best input pattern is (u-g, g-r, r-i, i-z, z-Y,Y-J, J-H, H-K, Y-K, g-z). In our case, the performance of kNN is assessed by different performance metrics, which indicate kNN has rather high performance for discriminating quasars from stars. As a result, kNN is an applicable and effective method to select quasar candidates for large sky survey projects.

Affiliations:

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000049
to stream PascalFrancis, to step Curation: 000196
to stream PascalFrancis, to step Checkpoint: 000172
to stream Main, to step Merge: 000B71
to stream Main, to step Curation: 000B65

Le document en format XML

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<front><div type="abstract" xml:lang="en">K-Nearest Neighbor (kNN) algorithm is one of the simplest and most flexible and effective classification algorithms, which has been widely used in many fields. Using the multi-band samples extracted from large surveys of SDSS DR7 and UKIDSS DR3, we investigate the performance of kNN with different combinations of colors to select quasar candidates. The color histograms of quasars and stars is helpful to select the optimal input pattern for the classifier of kNN. The best input pattern is (u-g, g-r, r-i, i-z, z-Y,Y-J, J-H, H-K, Y-K, g-z). In our case, the performance of kNN is assessed by different performance metrics, which indicate kNN has rather high performance for discriminating quasars from stars. As a result, kNN is an applicable and effective method to select quasar candidates for large sky survey projects.</div>
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A Simple and Effective Algorithm for Quasar Candidate Selection

A Simple and Effective Algorithm for Quasar Candidate Selection

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri