Paper
19 December 2002 A New Automated Classification Technique of Galaxy Spectra with Z<1.2 Based on PCA-ODP
Author Affiliations +
Abstract
In this paper, we investigate the Principal Component Analysis-Optimal Discrimination Plane (PCA-ODP) approach on a data set of galaxy spectra including eleven standard subtypes with the redshift value ranging from 0 to 1.2 and a span of 0.001. These eleven subtypes are E, S0, Sa, Sb, Sc, SB1, SB2, SB3, SB4, SB5, SB6, respectively, according to the Hubble sequence. Among them, the first four subtypes belong to the class of normal galaxies (NGs); the remaining seven belong to active galaxies (AGs). We apply the PCA approach to extract the features of galaxy spectra, project the samples onto the PCs, and investigate the ODP method on the data of feature space to find the optimal discrimination plane of the two main classes. ODP approach was developed from Fisher's linear discriminant method. The difference between them is that Fisher's method uses only one Fisher's vector and ODP uses two orthogonal vectors including Fisher's vector and another. Besides the data set above, we also use the Sloan Digital Sky Survey (SDSS) galaxy spectra and Kennicutt (1992) galaxy data to test the ODP classifier. The experiment results show that our proposed technique is both robust and efficient. The correct rate can reach as high as 99.95% for the first group data, 96% for SDSS data and 98% for Kennicutt data.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dongmei Qin, Zhanyi Hu, and Yongheng Zhao "A New Automated Classification Technique of Galaxy Spectra with Z<1.2 Based on PCA-ODP", Proc. SPIE 4847, Astronomical Data Analysis II, (19 December 2002); https://doi.org/10.1117/12.460379
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Galactic astronomy

Silver

Principal component analysis

Associative arrays

Astronomy

Feature extraction

Antimony

Back to Top