NDLI: Multiple Nonlinear Subspace Methods Using Subspace-based Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Kitamura, T. Abe, S. Tanaka, Y.
Copyright Year	2011
Abstract	In this paper, we propose multiple nonlinear subspace methods (MNSMs), in which each class consists of several subspaces with different kernel parameters. For each class and each candidate kernel parameter, we generate the subspace by KPCA, and obtain the projection length of an input vector onto each subspace. Then, for each class, we define the discriminant function by the sum of the weighted lengths. These weights in the discriminant function are optimized by subspace-based support vector machines (SS-SVMs) so that the margin between classes is maximized while minimizing the classification error. Thus, we can weight the subspaces for each class from the standpoint of class separability. Then, the computational cost of the model selection of MNSMs is lower than that of SS-SVMs because for SS-SVMs two hyper-parameters, which are the kernel parameter and the margin parameter, must be chosen before training. We show the advantages of the proposed method by computer experiments with benchmark data sets.
Starting Page	358
Ending Page	363
File Size	258803
Page Count	6
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy support vector machine Computational modeling Training data kernel method Vectors Eigenvalues and eigenfunctions subspace method Kernel
Content Type	Text
Resource Type	Article

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