Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy
Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy
复制标题
DOI：
10.1088/0957-4484/20/8/085714
复制
发表时间：
2009-02-25
期刊：
影响因子：
3.5
通讯作者：
Kalinin, Sergei V.
中科院分区：
文献类型：
--
作者：
Jesse, Stephen;Kalinin, Sergei V.
探索了一种基于主成分分析(PCA)的多维光谱成像数据分析方法。主成分分析根据数据中的方差选择相关响应分量并对其进行排序。结果表明，对于谱之间相对变化较小的例子，前几个主成分与用模型拟合得到的结果非常吻合，并且以大约4个数量级的速度实现。对于响应变化较大的情况，主成分分析提供了一种快速处理、去噪和压缩数据的有效方法。讨论了主成分分析和分量图的相关函数分析相结合作为显微镜数据分析和表示的通用工具的前景。
An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.