نتایج جستجو برای: variance decomposition

تعداد نتایج: 203024  

2016
Zeyuan Allen Zhu Yuanzhi Li

We study k-SVD that is to obtain the first k singular vectors of a matrix A approximately. Recently, a few breakthroughs have been discovered on k-SVD: Musco and Musco [18] provided the first gap-free theorem for the block Krylov method, Shamir [20] discovered the first variance-reduction stochastic method, and Bhojanapalli et al. [6] provided the fastest O(nnz(A) + poly(1/ε))-type of algorithm...

Journal: :Computer Physics Communications 2007
Tommy Burch Christian Hagen

Applying domain decomposition to the lattice Dirac operator and the associated quark propagator, we arrive at expressions which, with the proper insertion of random sources therein, can provide improvement to the estimation of the propagator. Schemes are presented for both open and closed (or loop) propagators. In the end, our technique for improving open contributions is similar to the “maxima...

2007
Ulrike Grömping

Assigning shares of “relative importance” to each of a set of regressors is one of the key goals of researchers applying linear regression, particularly in sciences that work with observational data. Although the topic is quite old, advances in computational capabilities have led to increased applications of computer-intensive methods like averaging over orderings that enable a reasonable decom...

Journal: :IEEE Trans. Geoscience and Remote Sensing 2003
Mickaël Germain Goze B. Bénié Jean-Marc Boucher Samuel Foucher Ko Fung Kalifa Goita

Radar images can show great variability from pixel to pixel, which is an obstacle to effective processing. This variability, due to speckle created by the radar wave coherence, necessitates the use of more adapted filters. Previous studies have shown that multiresolution wavelet analysis yields better results but produces artefacts due to multiscale decomposition. This paper proposes a method t...

2006
B YOONKYUNG LEE YUWON KIM SANGJUN LEE

S The support vector machine has been a popular choice of classification method for many applications in machine learning. While it often outperforms other methods in terms of classification accuracy, the implicit nature of its solution renders the support vector machine less attractive in providing insights into the relationship between covariates and classes. Use of structured kernels c...

Journal: :Rel. Eng. & Sys. Safety 2003
Marzio Marseguerra Riccardo Masini Enrico Zio Giacomo Cojazzi

This paper illustrates a method for efficiently performing multiparametric sensitivity analyses of the reliability model of a given system. These analyses are of great importance for the identification of critical components in highly hazardous plants, such as the nuclear or chemical ones, thus providing significant insights for their risk-based design and management. The technique used to quan...

2016
Zeyuan Allen-Zhu Yuanzhi Li

We study k-SVD that is to obtain the first k singular vectors of a matrix A. Recently, a few breakthroughs have been discovered on k-SVD: Musco and Musco [19] proved the first gap-free convergence result using the block Krylov method, Shamir [21] discovered the first variance-reduction stochastic method, and Bhojanapalli et al. [7] provided the fastestO(nnz(A)+ poly(1/ε))-time algorithm using a...

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