DIMENSIONALITY REDUCTION IN A BAYESIAN OPTIMIZATION USING STACKED AUTOENCODERS

摘要

The present embodiments relate to reducing the input dimensions to a machine based Bayesian Optimization using stacked autoencoders. By way of introduction, the present embodiments described below include apparatuses and methods for preprocessing a digital input to a machine-based Bayesian Optimization to a lower the dimensional space of the input, thereby lowering the bounds of the Bayesian optimization. The output of the Bayesian Optimization is then projected back into the original dimensional space to determine input and output values in the original dimensional apace. As such, the optimization is performed by the machine in a lower dimension using the stacked autoencoder to constrain the input dimensions to the optimization.