天文学进展2012，Vol.30Issue(1)：94-105,12.
光谱数据挖掘中的特征提取方法
Feature Extracting Methods in Spectrum Data Mining
摘要
Abstract
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.关键词
光谱/数据挖掘/特征提取Key words
spectrum/data mining/feature extraction分类
天文与地球科学引用本文复制引用
李乡儒..光谱数据挖掘中的特征提取方法[J].天文学进展,2012,30(1):94-105,12.基金项目
国家自然科学基金(61075033) （61075033）
广东省自然科学基金(S2011010003348) （S2011010003348）
中国科学院模式识别国家重点实验室开放基金(201001060) （201001060）
华南师范大学教学改革项目(2009jg28) （2009jg28）
