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2018 | OriginalPaper | Chapter

Sparsification of Indefinite Learning Models

Authors : Frank-Michael Schleif, Christoph Raab, Peter Tino

Published in: Structural, Syntactic, and Statistical Pattern Recognition

Publisher: Springer International Publishing

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Abstract

The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier for indefinite learning problems, but with a non-sparse decision function. This very dense decision function prevents practical applications due to a costly out of sample extension. In this paper we provide a post processing technique to sparsify the obtained decision function of a Krĕin space SVM and variants thereof. We evaluate the influence of different levels of sparsity and employ a Nyström approach to address large scale problems. Experiments show that our algorithm is similar efficient as the non-sparse Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed.

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Footnotes
1
Obtained by evaluating k(x, y) for training points x, y.
 
2
A similar strategy for KSVM may be possible but is much more complicated because typically quite many points are support vectors and special sparse SVM solvers would be necessary.
 
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Metadata
Title
Sparsification of Indefinite Learning Models
Authors
Frank-Michael Schleif
Christoph Raab
Peter Tino
Copyright Year
2018
Book
Structural, Syntactic, and Statistical Pattern Recognition

Print ISBN: 978-3-319-97784-3

Electronic ISBN: 978-3-319-97785-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-97785-0_17

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