Cascade Support Vector Machines with Dimensionality Reduction
Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning b...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2015/216132 |
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| author | Oliver Kramer |
| author_facet | Oliver Kramer |
| author_sort | Oliver Kramer |
| collection | DOAJ |
| description | Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities. |
| format | Article |
| id | doaj-art-396efa349daf4c149c0d25a73c2d9bf6 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-396efa349daf4c149c0d25a73c2d9bf62025-02-03T01:29:13ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322015-01-01201510.1155/2015/216132216132Cascade Support Vector Machines with Dimensionality ReductionOliver Kramer0Computational Intelligence Group, University of Oldenburg, 26111 Oldenburg, GermanyCascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.http://dx.doi.org/10.1155/2015/216132 |
| spellingShingle | Oliver Kramer Cascade Support Vector Machines with Dimensionality Reduction Applied Computational Intelligence and Soft Computing |
| title | Cascade Support Vector Machines with Dimensionality Reduction |
| title_full | Cascade Support Vector Machines with Dimensionality Reduction |
| title_fullStr | Cascade Support Vector Machines with Dimensionality Reduction |
| title_full_unstemmed | Cascade Support Vector Machines with Dimensionality Reduction |
| title_short | Cascade Support Vector Machines with Dimensionality Reduction |
| title_sort | cascade support vector machines with dimensionality reduction |
| url | http://dx.doi.org/10.1155/2015/216132 |
| work_keys_str_mv | AT oliverkramer cascadesupportvectormachineswithdimensionalityreduction |