Journal of the Korean Data and Information Science Society
- 제18권4호
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- Pages.1135-1143
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- 2007
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- 1598-9402(pISSN)
The Comparison of Singular Value Decomposition and Spectral Decomposition
초록
The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.
키워드