PROCÉDÉS PERMETTANT DE RÉDUIRE LE TEMPS DE CALCUL PAR LE BIAIS D'UNE RÉDUCTION DE DIMENSIONNALITÉ

摘要

Dimensionality reduction in high-dimensional datasets can decrease computation time, and processes for dimensionality reduction may even be useful in lower-dimensional datasets. It has been discovered that methods of dimensionality reduction may dramatically decrease computational requirements in machine learning programming techniques. This development unlocks the ability of computational modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.