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Principal Component Analysis (PCA)-based techniques can separate data intodifferent uncorrelated components and facilitate the statistical analysis as apre-processing step. Independent Component Analysis (ICA) can separatestatistically independent signal sources through a non-parametric and iterativealgorithm. Non-negative matrix factorization is another PCA-similar approach tocategorizing dimensions in physically-interpretable groups. Singular spectrumanalysis (SSA) is a time-series-related PCA-like algorithm. After anintroduction and a literature review on processing JWST data from theNear-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI), potentialparts to intervene in the James Webb Space Telescope imaging data reductionpipeline will be discussed.

PCA-based Data Reduction and Signal Separation Techniques for James-Webb Space Telescope Data Processing