Fast optimization of Multithreshold Entropy Linear Classifier
This work addresses optimization efficiency for a specific classifier, representing an incremental improvement.
The paper tackled the slow optimization speed of the Multithreshold Entropy Linear Classifier (MELC) by proposing approximate methods and adaptive parameter selection to speed it up, achieving practical usability confirmed on 10 UCI datasets.
Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation. Despite its good empirical results, one of its drawbacks is the optimization speed. In this paper we analyze how one can speed it up through solving an approximate problem. We analyze two methods, both similar to the approximate solutions of the Kernel Density Estimation querying and provide adaptive schemes for selecting a crucial parameters based on user-specified acceptable error. Furthermore we show how one can exploit well known conjugate gradients and L-BFGS optimizers despite the fact that the original optimization problem should be solved on the sphere. All above methods and modifications are tested on 10 real life datasets from UCI repository to confirm their practical usability.