[Show abstract][Hide abstract] ABSTRACT: A kernel based covering algorithm, called the kernel covering algorithm (KCA), is proposed for the classification of celestial spectra. This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space. The experiments show that the classification result based on KCA is a little less than that based on SVM. However, KCA only involves the distance computation without the need to solve the quadratic programming problem. Also, KCA is insensitive to the width of gauss window. Although KCA has a comparable classification performance with the covering algorithm, it changes the distance between samples in feature space by the nonlinear mapping such that the distribution of samples is more adaptable to classify. Therefore, the number of KCA's resulting support vectors is significantly smaller than that of the covering algorithm.
No preview · Article · Apr 2007 · Guang pu xue yu guang pu fen xi = Guang pu
[Show abstract][Hide abstract] ABSTRACT: A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework. LDA, GDA, PCA and KPCA were experimentally compared with these three different kinds of spectra. Among these four techniques, GDA obtains the best result, followed by LDA, and PCA is the worst. Although KPCA is also a kernel based technique, its performance is not satisfactory if the selected number of the principal components is small, and in some cases, it appears even worse than LDA, a non-kernel based technique.
No preview · Article · Nov 2006 · Guang pu xue yu guang pu fen xi = Guang pu
[Show abstract][Hide abstract] ABSTRACT: This paper presents a fast neural network method of radial basis function with dynamic decay adjustment (RBFN-DDA) to classify
Quasi-Stellar Objects (QSOs) and galaxies automatically. The classification process is mainly comprised of three parts: (1)
the dimensions of the normalized input spectra is reduced by the Principal Component Analysis (PCA); (2) the network is built
from scratch: the number of required hidden units is determined during training and the individual radii of the Gaussians
are adjusted dynamically until corresponding criterions are satisfied; (3) The trained network is used for the classification
of the real spectra of QSOs and galaxies. The method of RBFN-DDA having constructive and fast training process solves the
difficulty of selecting appropriate number of neurons before training in many methods of neural networks and achieves lower
error rates of spectral classification. Besides, due to its efficiency, the proposed method would be particularly useful for
the fast and automatic processing of voluminous spectra to be produced from the large-scale sky survey project.