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...

To embed watermark is a way to increase the robustness of the image. In this paper the singular value decomposition (SVD) based image watermarking scheme is proposed. The output result of SVD is more secure and robust. In the proposed scheme D and U components are used for embedding watermark. Unlike other transforms which uses fixed orthogonal bases, SVD uses non fixed orthogonal bases. The re...

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...

Dimensionality reduction technique is applied to get rid of the inessential terms like redundant and noisy terms in documents. In this paper a systematic study is conducted for seven dimensionality reduction methods such as Latent Semantic Indexing (LSI), Random Projection (RP), Principle Component Analysis (PCA) and CUR decomposition, Latent Dirichlet Allocation(LDA), Singular value decomposit...

In this work we show when and how techniques based on the Singular Value Decomposition (SVD) and the QR decomposition of a fundamental matrix solution can be used to infer if a system enjoys –or not– exponential dichotomy on the whole real line.

Digital watermarking is one of the techniques for copyright protection. In this paper, a normalization-based robust image watermarking scheme which encompasses singular value decomposition (SVD) and discrete cosine transform (DCT) techniques is proposed. For the proposed scheme, the host image is first normalized to a standard form and divided into non-overlapping image blocks. SVD is applied t...

A reduced order representation of a large data set is often realized through a principal component analysis based upon a singular value decomposition (SVD) of the data. The left singular vectors of a truncated SVD provide the reduced basis. In several applications such as facial analysis and protein dynamics, structural symmetry is inherent in the data. Typically, reflective or rotational symme...

Two new methods for solving the symmetric saddle point problem are proposed. The first one is a generalization of Golub’s method for the augmented system formulation (ASF) and uses the Householder QR decomposition. The second method is supported by the singular value decomposition (SVD). Numerical comparison of some direct methods are given.

This paper presents an approach to design observer-based fault detection (FD) systems for continuous linear time-invariant systems directly from frequency domain data. The design doesn’t need knowledge of system model. The computation mainly consists in a singular value decomposition (SVD) and a QR decomposition. The proposed approach is finally illustrated by an example. Copyright c ©2005 IFAC