Reliability checks on the Indo-US Stellar Spectral Library using Artificial Neural Networks and Principal Component Analysis

The Indo-US coud\'{e} feed stellar spectral library (CFLIB) made available to the astronomical community recently by Valdes et al. (2004) contains spectra of 1273 stars in the spectral region 3460 to 9464 \AA at a high resolution of 1 \AA FWHM and a wide range of spectral types. Cross-checking the reliability of this database is an important and desirable exercise since a number of stars in this database have no known spectral types and a considerable fraction of stars has not so complete coverage in the full wavelength region of 3460-9464 \AA resulting in gaps ranging from a few \AA to several tens of \AA. In this paper, we use an automated classification scheme based on Artificial Neural Networks (ANN) to classify all 1273 stars in the database. In addition, principal component analysis (PCA) is carried out to reduce the dimensionality of the data set before the spectra are classified by the ANN. Most importantly, we have successfully demonstrated employment of a variation of the PCA technique to restore the missing data in a sample of 300 stars out of the CFLIB.Comment: 17 pages, 8 figures PASJ Vol.58, No1 (it will be issued on February 25, 2006

Identification of Peculiar Data by Using Restoration Method Based on Principal Component Analysis

We have developed the restoration method for missing data based on Principal Component Analysis in the previous issues (Yuasa et al. 2005; 2006). From another point of view, this method is able to be regarded as a tool to distinguish a peculiar data from the other most of the data which can be classified normally. We show some examples in the study of classification of the stellar spectra.本文データは一部CiNiiから複製したものである

Restoration of Missing Data and Reconstruction of Dynamical Systems

[Abstract] The eliminated data in Lorentz dynamical system is restored by using the generalized Principal Component Analysis (PCA). The restored data and the original data which has been eliminated, are compared. Both data have good coincidence. Adopting the original data only (case 1) and the data which includes partly the restored one (case 2), the reconstruction of Lorentz dynamical system, i.e. the system of the original differential equations, is examined respectively by the method of repeated PCA.本文データの一部は、CiNiiから複製したものである

Supplementation of Adjusted Values to the Imperfect Data

It happens frequently that the observational data is not complete but missing partly by various reasons. A preliminary study for supplementing adjusted values to such imperfect data based on Principal Component Analysis (PCA) is executed. IRAS 3 colors of mass-losing stars and their expanding velocity on the ground based observations are adopted for the experiment. One of these 4 data is eliminated for each star and the adjusted value is restored. The original data and the restored one are compared and the distribution of the restored errors is studied.本文データの一部は、CiNiiから複製したものである

Reliability Checks on the Indo-US Stellar Spectral Library Using Artificial Neural Networks and Principal Component Analysis

[Abstract] The Indo-US coude feed stellar spectral library (CFLIB) made available to the astronomical community recently by Valdes et al. (2004, ApJS, 152, 251) contains spectra of 1273 stars in the spectral region 3460 to 9464A at a high resolution of 1A (FWHM) and a wide range of spectral types. Cross-checking the reliability of this database is an important and desirable exercise since a number of stars in this database have no known spectral types and a considerable fraction of stars has not so complete coverage in the full wavelength region of 3460–9464A resulting in gaps ranging from a few A to several tens of A. We use an automated classification scheme based on Artificial Neural Networks (ANN) to classify all 1273 stars in the database. In addition, principal component analysis (PCA) is carried out to reduce the dimensionality of the data set before the spectra are classified by the ANN. Most importantly, we have successfully demonstrated employment of a variation of the PCA technique to restore the missing data in a sample of 300 stars out of the CFLIB.Copyright (c) 2006 Astronomical Society of Japa