Feature Extracting Methods in Spectrum Data Mining
Xiangru Li · Progress in Astronomy · 2012
Feature extraction is the fundamental step in spectrum data mining,which determines both the quality of the mining results and the efficiency,robustness,complexity of the mining system.This work reviews the current state of celestial spectrum feature extracting methods,introducs the fundamental ideas,analyzes their superiorities,limitations and applicabilities.By extracting features,the measurements of a spectrum are decomposed, reorganized and selected.Based on the characteristics of information expression,we classify the available feature extraction methods into three categories:statistical reduction method, characteristic spectrum method,and spectral line method.Their applications in spectrum data mining are also introduced.For clarity,the statistical reduction method is further classified into the following four classes:principal component analysis(PCA),wavelet transform (WT),manifold learning and supervised methods.In addition,we also study such characteristics of these methods as time-frequency analysis,the interpretability of physical meaning,robustness to calibration distortion,robustness to outlier,etc.