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Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
[article]
2021
arXiv
pre-print
Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has been introduced as a solution to this problem. Most of the existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and
arXiv:2012.00560v2
fatcat:bnb7vtzrabcglexgfjjyis7eke