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Sparse PARAFAC fitting for incomplete multi-way data via Levenberg-Marquardt
NIPALS performs PCA on data using iterative least squares and deflation steps. It can deal with missing values.
This code recovers saturated signals using O-ALS by treating saturated signal as NaNs and reconstructing them from PCA subspace.
OALS calculates the scores and loadings from data with missing values.
Iterative SVD performs iterative PCA-based imputation to handle missing data.
A Whittaker smoother using penalized LS & iterative imputation to smooth data & fill gaps.