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It involves transformation of\nfunctional predictors into several candidate explanatory\nmatrices (based on contingency tables), to which relative edge\nmatrices with contiguity constraints are associated.\nGeneralized Fused Lasso regression are performed in order to\nidentify the best candidate matrix, the best class intervals\nand related coefficients at each iteration. The approach is\nstopped when the maximal number of iterations is reached or\nwhen retained coefficients are zeros. 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