Cascaded Dimensionality Reduction Method and Its Application in Spectral Classification

Abstract

The classification of the high-dimensional spectral is one of the important study domains in the astronomy. However, the curse of dimension problem restrains the performance of the methods to classify the spectral data. In this paper, the cascaded dimensionality reduction, combining with the virtues of the principal component analysis and t-distributed stochastic neighbour embedding, is conducted to improve the performance of classification methods for spectral data. In the cascaded dimensionality reduction, the PCA is employed to pre-reduce dimensions of spectral data for reducing redundant information, under the constraint of preserving the information integrity as far as possible; T-SNE highlights the differences among the samples with different labels, and outputs target results after the dimension reduction. The support vector machine in conjunction with the cascaded dimensionality reduction is applied to classify the spectral data, and its performance is compared with the PCA based SVM and T-SNE based SVM. Experimental results demonstrate that the cascaded dimensionality reduction assists the SVM obtaining better performance than PCA and T-SNE.