Fast Optimization of Multithreshold Entropy Linear Classifier

Alternative titles

Abstract

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.

Keywords

multithreshold classifier, entropy, approximation, optimization

References

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Information

Information: Schedae Informaticae, 2014, pp. 57 - 67

DOI: https://doi.org/10.4467/20838476SI.14.005.3022

Article type: Original article

Titles:

Polish:

Fast Optimization of Multithreshold Entropy Linear Classifier

English:

Fast Optimization of Multithreshold Entropy Linear Classifier

Authors

Rafał Józefowicz

Google Inc.

Wojciech Marian Czarnecki

Department of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland

Published at: 14.04.2015

Article status: Öffnen Sie

Licence: None

Percentage share of authors:

Rafał Józefowicz (Author) - 50%

Wojciech Marian Czarnecki (Author) - 50%

Article corrections:

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Publication languages:

Englisch

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Number of downloads: 1735