Abstract
Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functions. A simulation study as well as an application to kneading data compare the PLS model results with those given by other methods.
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Preda, C., Saporta, G. & Lévéder, C. PLS classification of functional data. Computational Statistics 22, 223–235 (2007). https://doi.org/10.1007/s00180-007-0041-4
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DOI: https://doi.org/10.1007/s00180-007-0041-4