Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/61536


Title: Split-and-combine singular value decomposition for large-scale matrix
Authors: 曾正男
Tzeng,Jengnan
Contributors: 應數系
Date: 2013.04
Issue Date: 2013-11-08 11:59:23 (UTC+8)
Abstract: The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order three computational cost makes many modern applications infeasible, especially when the scale of the data is huge and growing. Therefore, it is imperative to develop a fast SVD method in modern era. If the rank of matrix is much smaller than the matrix size, there are already some fast SVD approaches. In this paper, we focus on this case but with the additional condition that the data is considerably huge to be stored as a matrix form. We will demonstrate that this fast SVD result is sufficiently accurate, and most importantly it can be derived immediately. Using this fast method, many infeasible modern techniques based on the SVD will become viable.
Relation: Journal of Applied Mathematics, Vol.0, No.0
Data Type: article
Appears in Collections:[應用數學系] 期刊論文

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