Authors:
Fabian Gieseke
1
;
Antti Airola
2
;
Tapio Pahikkala
2
and
Oliver Kramer
1
Affiliations:
1
Carl von Ossietzky Universität Oldenburg, Germany
;
2
TUCS and University of Turku, Finland
Keyword(s):
Semi-supervised support vector machines, Non-convex optimization, Quasi-Newton methods, Sparse data, Nyström approximation.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Kernel Methods
;
Large Margin Methods
;
Pattern Recognition
;
Sparsity
;
Theory and Methods
Abstract:
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.
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