A majority of DNA microarray datasets contain missing or corrupt values and it is critical to estimate these values accurately. These missing values are most often attributed to insufficient experimental resolution or the presence of foreign objects on the experimental slide’s surface. To improve existing missing value estimation algorithms, this paper introduces and investigates the scalable s...

This paper proposes a novel signature based on singular value decomposition (SVD) for video sequence matching. By considering the input image as a matrix, a partition procedure is first performed to separate the matrix into non-overlapping sub-images of a fixed size. The SVD process then individually decomposes each partitioned sub-image into an singular value and the corresponding singular vec...

In matrix algebra, the Singular value decomposition (SVD) is an factorization of complex matrix that has been applied to principal component analysis, canonical correlation in statistics, the determination of the low rank approximation of matrices. In this paper, using the SVD and the theory of low rank approximation of a matrix, we offer a new scheme for color image compression based on singul...

The singular value decomposition (SVD) is not only a classical theory in matrix computation and analysis, but also is a powerful tool in machine learning and modern data analysis. In this tutorial we first study the basic notion of SVD and then show the central role of SVD in matrices. Using majorization theory, we consider variational principles of singular values and eigenvalues. Built on SVD...

in this paper, we use modified laplace decomposition method to solving initial value problems (ivp) of the second order ordinary differential equations. theproposed method can be applied to linear and nonlinearproblems

In order to improve the robustness and imperceptibleness of the algorithm, a new embedding and extracting method with DWT-SVD is proposed. The approximation matrix of the third level of image in DWT domain is modified with SVD to embed the singular value of watermark to the singular value of DWT coefficient. The proposed embedding and extracting method was employed to accelerate the hybrid DWT-...

This paper focuses on a new extension version of double Divide and Conquer (dDC) algorithm to eigen decomposition. Recently, dDC was proposed for singular value decomposition (SVD) of rectangular matrix. The dDC for SVD consists of two parts. One is Divide and Conquer (D&C) for singular value and the other is twisted factorization for singular vector. The memory usage of dDC is smaller than tha...

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

In this paper a robust Hybridized Watermarking scheme based on Fast Walsh Hadamard transform (FWHT) and Singular Value Decomposition (SVD) using Genetic algorithm (GA) is presented. The host image is subjected to FWHT and SVD . The singular values of SVD of host image are modified with singular values of watermark. Multiple scaling factors are used in watermark embedding. The GA searches for op...

The Netflix movie-recommendation problem was investigated and the incremental Singular Value Decomposition (SVD) algorithm was implemented to solve the problem. Experimental results are discussed.