Principal Component Analysis of Spectroscopic Imaging Data in Scanning Probe Microscopy

Abstract

The approach for data analysis in band excitation family of scanning probe microscopies based on principal component analysis (PCA) is explored. PCA utilizes the similarity between spectra within the image to select the relevant response components. For small signal variations within the image, the PCA components coincide with the results of deconvolution using simple harmonic oscillator model. For strong signal variations, the PCA allows effective approach to rapidly process, de-noise and compress the data. The extension of PCA for correlation function analysis is demonstrated. The prospects of PCA as a universal tool for data analysis and representation in multidimensional SPMs are discussed.