Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 28, Issue 1, Pages 69-74Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2006.17
Keywords
support vector machines; proximal classification; generalized eigenvalues
Funding
- NLM NIH HHS [5 T15 LM 07359-02] Funding Source: Medline
- NATIONAL LIBRARY OF MEDICINE [T15LM007359] Funding Source: NIH RePORTER
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A new approach to support vector machine (SVM) classification is proposed wherein each of two data sets are proximal to one of two distinct planes that are not parallel to each other. Each plane is generated such that it is closest to one of the two data sets and as far as possible from the other data set. Each of the two nonparallel proximal planes is obtained by a single MATLAB command as the eigenvector corresponding to a smallest eigenvalue of a generalized eigenvalue problem. Classification by proximity to two distinct nonlinear surfaces generated by a nonlinear kernel also leads to two simple generalized eigenvalue problems. The effectiveness of the proposed method is demonstrated by tests on simple examples as well as on a number of public data sets. These examples show the advantages of the proposed approach in both computation time and test set correctness.
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