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Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines

Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer, Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines. In: Pedro Latorre Carmona, J. Salvador Sánchez, Ana L. N. Fred (Eds.), Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM), 45–54, SciTePress, 2012.

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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BibTeX entry:

@INPROCEEDINGS{inpGiAiPaKr12a,
  title = {Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines},
  booktitle = {Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
  author = {Gieseke, Fabian and Airola, Antti and Pahikkala, Tapio and Kramer, Oliver},
  editor = {Latorre Carmona, Pedro and Sánchez, J. Salvador and Fred, Ana L. N.},
  publisher = {SciTePress},
  pages = {45–54},
  year = {2012},
  keywords = {Machine learning, Semi-Supervised Support Vector Machines, Non-Convex Optimization, Quasi-Newton Methods, Sparse Data, Nyström Approximation},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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