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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.

Since the off-diagonal regions are used to store the transform information, this approach is very efficient in saving the computational memory.

If we only want to compute a few of the largest singular values and associated singular vectors of a large matrix, the Lanczos bidiagonalization is an important procedure for solving this problem [5–8].

One of the key approaches of the MDS is simply the SVD, that is, if we can find a fast approach of the MDS then it is possible to find a fast approach of the SVD.

When the data configuration is Euclidean, the MDS is similar to the PCA, in that both can remove inherent noise with its compact representation of data.

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.

In this paper, we would like to implement SCMDS to the fast SVD approach, say SCSVD.The following subsections are reviews of the classical MDS and the SCSVD.Assume that computational complexity makes it infeasible.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.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.Currently there are some well-known methods for computing the SVD.For example, the GR-SVD is a two-step method which performs Householder transformations to reduce the matrix to bidiagonal form then performs the QR iteration to obtain the singular values [3, 4].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.


  1. Abstract. This paper develops an identity for additive modivations of a singular value decomposition. SVD to reflect updates, downdates, shifts, and edits of the data matrix. This sets the stage for fast and ememory-efficient sequential algorithms for tracking singular values and subspaces. In conjunction with a fast solution.

  2. Incremental SVD. Motivation. In many applications, the production of the matrix A happens incrementally. This has motivated numerous methods for SVD updating. e.g. Businger; Bunch,Nielson. Benefits. Latency in producing new columns of A can be amortized in the SVD update. “Online” SVD is useful/necessary in some.

  3. Mar 24, 2014. Abstract—The singular value decomposition SVD is an im- portant tool for subspace estimation. In adaptive signal process- ing, we are especially interested in tracking the SVD of a recursively updated data matrix. This paper introduces a new tracking technique, designed for rectangular sliding window.

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