Sparse quasi-Newton optimization for semi-supervised support vector machines

TY - GEN

T1 - Sparse quasi-Newton optimization for semi-supervised support vector machines

AU - Gieseke, Fabian

AU - Airola, Antti

AU - Pahikkala, Tapio

AU - Kramer, Oliver

N1 - Conference code: 1

PY - 2012

Y1 - 2012

N2 - In real-world scenarios, labeled data is often rare while unlabeled data can be obtained in huge quantities. A current research direction in machine learning is the concept of semi-supervised support vector machines. This type of binary classification approach aims at taking the additional information provided by unlabeled patterns into account to reveal more information about the structure of the data and, hence, to yield models with a better classification performance. However, generating these semi-supervised models requires solving difficult optimization tasks. In this work, we present a simple but effective approach to address the induced optimization task, which is based on a special instance of the quasi-Newton family of optimization schemes. The resulting framework can be implemented easily using black box optimization engines and yields excellent classification and runtime results on both artificial and real-world data sets that are superior (or at least competitive) to the ones obtained by competing state-of-the-art methods.

AB - In real-world scenarios, labeled data is often rare while unlabeled data can be obtained in huge quantities. A current research direction in machine learning is the concept of semi-supervised support vector machines. This type of binary classification approach aims at taking the additional information provided by unlabeled patterns into account to reveal more information about the structure of the data and, hence, to yield models with a better classification performance. However, generating these semi-supervised models requires solving difficult optimization tasks. In this work, we present a simple but effective approach to address the induced optimization task, which is based on a special instance of the quasi-Newton family of optimization schemes. The resulting framework can be implemented easily using black box optimization engines and yields excellent classification and runtime results on both artificial and real-world data sets that are superior (or at least competitive) to the ones obtained by competing state-of-the-art methods.

U2 - 10.5220/0003755300450054

DO - 10.5220/0003755300450054

M3 - Article in proceedings

SN - 978-989-8425-98-0

SP - 45

EP - 54

BT - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012)

A2 - Carmona, Pedro Latorre

A2 - Sánchez, J. Salvador

A2 - Fred, Ana

PB - SCITEPRESS Digital Library

T2 - 1st International Conference on Pattern Recognition Applications and Methods

Y2 - 6 February 2012 through 8 February 2012

ER -