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A New Support Vector Compression Method Based on Singular Value Decomposition
Sang-Hun Yoon, Chun-Gi Lyuh, Ik-Jae Chun, Jung-Hee Suk, and Tae Moon Roh
Abstract :	In this letter, we propose a new compression method for a high dimensional support vector machine (SVM). We used singular value decomposition (SVD) to compress the norm part of a radial basis function SVM. By deleting the least significant vectors that are extracted from the decomposition, we can compress each vector with minimized energy loss. We select the compressed vector dimension according to the predefined threshold which can limit the energy loss to design criteria. We verified the proposed vector compressed SVM (VCSVM) for conventional datasets. Experimental results show that VCSVM can reduce computational complexity and memory by more than 40% without reduction in accuracy when classifying a 20,958 dimension dataset.
Key word :	RBF SVM, SVD, vector compression.
DOI :	http://dx.doi.org/10.4218/etrij.11.0210.0349
Cite this :	Sang-Hun Yoon, Chun-Gi Lyuh, Ik-Jae Chun, Jung-Hee Suk, and Tae Moon Roh, "A New Support Vector Compression Method Based on Singular Value Decomposition," ETRI Journal, vol. 33, no. 4, Aug. 2011, pp. 652-655. http://dx.doi.org/10.4218/etrij.11.0210.0349
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