Moving Window Principal Component Analysis for Detecting Positional Fluctuation of Spectral Changes
作者： Soo-Ryeon Ryu , Isao Noda , Young-Mee Jung
DOI: 10.5012/BKCS.2011.32.7.2332
关键词:
摘要: In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as sensitive diagnostic tool to detect the presence peak position shift. approach, is constructed from small data segment along wavenumber axis. For each bound by narrow region, separate PCA was applied. Simulated spectra with complex spectral feature variations were analyzed explore possibility MWPCA technique. This MWPCA-based detection shift, potentially coupled 2D correlation provide additional verification, may offer an attractive solution.
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