2 research outputs found
Fast optimization of Multithreshold Entropy Linear Classifier
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.Comment: Presented at Theoretical Foundations of Machine Learning 2015
(http://tfml.gmum.net), final version published in Schedae Informaticae
Journa
Fast optimization of multithreshold entropy linear classifier
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