This paper presents a dynamic image approach to characterize the growth of brain cancer invasion of tumor gliomas cells using singular value decomposition (SVD) technique. Such a dynamic image is identified by the white and grey matter displayed by magnetic resonance (MR) images of the patient brain taken at different times. SVD components and properties have been analyzed for different brain i...

Steganography is the study of data hiding for the purpose of covert communication. A secret message is inserted into a cover file so that the very existence of the message is not apparent. Most current steganography algorithms insert data in the spatial or transform domains; common transforms include the discrete cosine transform, the discrete Fourier transform, and discrete wavelet transform. ...

SUMMARY We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude ...

In this paper a parallel implementation of the SVD{updating algorithm using approximate rotations is presented. In its original form the SVD{updating algorithm had numerical problems if no reorthogonalization steps were applied. Representing the orthogonal matrix V (right singular vectors) using its parameterization in terms of the rotation angles of n(n?1)=2 plane rotations these reorthogonali...

Many important applications – from big data analytics to information retrieval, gene expression analysis, and numerical weather prediction – require the solution of large dense singular value decompositions (SVD). In many cases the problems are too large to fit into the computer’s main memory, and thus require specialized out-of-core algorithms that use disk storage. In this paper, we analyze t...

This paper discusses clustering and latent semantic indexing (LSI) aspects of the singular value decomposition (SVD). The purpose of this paper is twofold. The first is to give an explanation on how and why the singular vectors can be used in clustering. And the second is to show that the two seemingly unrelated SVD aspects actually originate from the same source: related vertices tend to be mo...

The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, highdimensional 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 ...

Diabetes patients have more than double the risk of ischemic stroke compared with non-diabetic individuals, and its neuroimaging characteristics have important clinical implications. To understand the pathophysiology of ischemic stroke in diabetes, it is important to focus not only on the stroke subtype, but also on the size and location of the occlusive vessels. Specifically, ischemic stroke i...

This thesis presents a systolic algorithm for the SVD of arbitrary complex matrices, based on the cyclic Jacobi method with \parallel ordering". As a basic step in the algorithm, a two-step, two-sided unitary transformation scheme is employed to diagonalize a complex 2 2 matrix. The transformations are tailored to the use of CORDIC (COordinate Rotation Digital Computer) algorithms for high spee...

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...