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Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as "X"<sub>"i"</sub>("t")&equals;"μ"("t")&plus;Σ<sub>1≤"l"&infin ;</sub>"η"<sub>"i", "l"</sub>&plus; "v"<sub>"l"</sub>("t "), where "μ" is the common mean, "v"<sub>"l"</sub> are the eigenfunctions of the covariance operator and the...</sub>
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