Improving the Efficiency of the Support Vector Decomposition Machine

Abstract

The Support Vector Decomposition Machine is a supervised dimensionality reduction technique which simultaneously minimizes reconstruction error and classification loss. To guarantee a unique minimum, a set of arbitrary constraints are introduced. We propose a different set of constraints, which result in a much more efficient implementation, drastically reducing both training and inference time in simulations with synthetic data.