Svd updating when do bones and booth start dating

Therefore, we need a fast MDS method to deal with the huge data problem.The MDS method is useful in the application of dimension reduction.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

The main purpose of this paper is to deal with this problem when the numerical rank of the huge matrix is small.

The second purpose of this paper is to update the SVD when the matrix size is extended by new data updating.

However, the previously mentioned methods all require matrix multiplications for the SVD.

One interesting problem is how do we compute the SVD for a matrix when the matrix size is huge and loading the whole matrix into the memory is not possible?

Department of Mathematical Sciences, National Chengchi University, No.

64, Section 2, Zhi Nan Road, Wenshan District, Taipei City 11605, Taiwan Received 16 November 2012; Revised 17 January 2013; Accepted 22 January 2013Academic Editor: Nicola Mastronardi Copyright © 2013 Jengnan Tzeng.

In recent years, digital information has been proliferating and many analytic methods based on the PCA and the SVD are facing the challenge of their significant computational cost.

Thus, it is crucial to develop a fast approach to compute the PCA and the SVD.

If the matrix [13], in which we have proved that when the data dimension is significantly smaller than the number of data entries, there is a fast linear approach for the classical MDS.

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