The Singular Value Decomposition is a key operation in many machine learning methods. Its computational cost, however, makes it unscalable and impractical for applications involving large datasets or real-time responsiveness, which are becoming increasingly common. We present a new method, QUIC-SVD, for fast approximation of the whole-matrix SVD based on a new sampling mechanism called the cosi...

The main goal of this paper is to embed a watermark in the speech signal, using the three techniques such as Discrete Cosine Transform (DCT) along with Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT).In this paper, various combinations were tried for embedding the watermark image into the audio signal such as DWT and SVD, DCT with SVD and DCT, DWT with SVD. Their perform...

Privacy protection is indispensable in data mining, and many privacy-preserving data mining (PPDM) methods have been proposed. One such method is based on singular value decomposition (SVD), which uses SVD to find unimportant information for data mining and removes it to protect privacy. Independent component analysis (ICA) is another data analysis method. If both SVD and ICA are used, unimport...

The Singular Value Decomposition (SVD) is an important tool for linear algebra and can be used to invert or approximate matrices. Although many authors use "SVD" synonymously with "Eigenvector Decomposition" or "Principal Components Transform", it is important to realize that these other methods apply only to symmetric matrices, while the SVD can be applied to arbitrary nonsquare matrices. This...

Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. In this work we introduce a new approach to improve the preciseness of the standard Quantum Fourier Transform. The presented Quantum-SVD algorithm is based on the singular value dec...

In this paper, we will demonstrate three-dimensional tomographic reconstruction of space-borne highresolution SAR data using Shanghai as our test site. The high density of high-rise buildings in Shanghai leads to a rather complicated backscattering regime, which is difficult to handle with conventional interferometric processing. For the tomographic signal reconstruction, we use three different...

Golub and Loan (1980) presented a numerically-stable TLS algorithm which utilizes the singular value decomposition (SVD). Subsequent refinements to the method predominantly use SVD, and much of the current literature emphasizes stabilization of the inverse and implicit model regularization by SVD truncation (Fierro et al., 1997). Because it is numerically intensive, however, the SVD generally p...

The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the...

Singular value decomposition (SVD) is a useful multivariate technique for dimension reduction. It has been successfully applied to analyze microarray data, where the eigen vectors are called eigen-genes/arrays. One weakness associated with the SVD is the interpretation. The eigen-genes are essentially linear combinations of all the genes. It is desirable to have sparse SVD, which retains the di...

We demonstrate an implementation for an approximate rank-k SVD factorization, combiningwell-known randomized projection techniques with previously implemented map/reduce solutions in order to compute steps of the random projection based SVD procedure, such QR and SVD. We structure the problem in a way that it reduces to Cholesky and SVD factorizations on k× k matrices computed on a single machi...