[Supervised feature extraction based on FDA and galaxy spectra classification].
With the recent technological advances in wide field survey astronomy and the implementation of several large scale astronomical survey proposals, celestial spectra are becoming very rich and the study of automated processing methods is attracting more and more attention. In the present work, the authors pointed out that it is necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in literature and the limitations of unsupervised feature extraction methods. And the authors studied supervised feature extraction based on Fisher discriminant analysis (FDA) and its application in galaxy spectra classification. FDA could effectively reduce dimension and extract the features based on the classifying capability by fusing information in training data. Experiments show its superior performance in dimensional reduction for galaxy spectra classification.
Li
Xiang-Ru
XR
Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
Hu
Zhan-Yi
ZY
Zhao
Yong-Heng
YH