Results 1  10
of
141,290
Deflated and augmented Krylov subspace techniques
 Numer. Linear Algebra Appl
, 1996
"... We present a general framework for a number of techniques based on projection methods on `augmented Krylov subspaces'. These methods include the deflated GMRES algorithm, an innerouter FGMRES iteration algorithm, and the class of block Krylov methods. Augmented Krylov subspace methods often ..."
Abstract

Cited by 75 (11 self)
 Add to MetaCart
We present a general framework for a number of techniques based on projection methods on `augmented Krylov subspaces'. These methods include the deflated GMRES algorithm, an innerouter FGMRES iteration algorithm, and the class of block Krylov methods. Augmented Krylov subspace methods
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
Abstract

Cited by 514 (20 self)
 Add to MetaCart
We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a
Krylov Subspace Techniques for ReducedOrder Modeling of Nonlinear Dynamical Systems
 Appl. Numer. Math
, 2002
"... Means of applying Krylov subspace techniques for adaptively extracting accurate reducedorder models of largescale nonlinear dynamical systems is a relatively open problem. There has been much current interest in developing such techniques. We focus on a bilinearization method, which extends Kry ..."
Abstract

Cited by 89 (5 self)
 Add to MetaCart
Means of applying Krylov subspace techniques for adaptively extracting accurate reducedorder models of largescale nonlinear dynamical systems is a relatively open problem. There has been much current interest in developing such techniques. We focus on a bilinearization method, which extends
ROBUST SUBSPACE TECHNIQUE FOR JOINT ANGLE/DOPPLER ESTIMATION IN
"... We consider the problem of joint angle and doppler estimation for SpaceTime Adaptive Processing (STAP) airborne radar in nongaussian clutter which is modeled as a complex symmetric alpha stable SαS process. We introduce a sign covariance estimate which has almost robust performance in heavy tailed ..."
Abstract
 Add to MetaCart
noise [1]. The subspace estimate is calculated via the propagator method [2] to reduce the computational load in the way that it does not require the eigendecomposition. Performance of the proposed technique is assessed through simulations and it is shown that the method reveals better performance than
Subspace Techniques for RadioAstronomical Data Enhancement
, 809
"... Radio astronomical observations have very poor signal to noise ratios, unlike in other disciplines. On the other hand, it is possible to observe the object of interest for long time intervals as well as using a wider bandwidth. Traditionally, by averaging in time and in frequency, it has been possib ..."
Abstract
 Add to MetaCart
, due to intrinsic variation of the sky as well as due to errors generated by the instrument. In this paper, we shall discuss an alternative to averaging of images, without ignoring subtle changes in the observed data over time and frequency, using subspace decomposition. By separation of data to signal
SemiBlind Subspace Techniques For Digital Communication Systems
"... This paper is devoted to the analysis of a "semiblind" estimation framework in which the standard inputoutput (training sequence based) estimation is enhanced by using the statistical structure of the information sequence. More specifically, we consider the case of a general TDMA frameb ..."
Abstract
 Add to MetaCart
based receiver equipped with multiple sensors, and restrict our attention to secondorder based subspace methods which are suitable for most standard communication applications due to their moderate computational cost. The channel estimator is obtained as a regularized leastsquares solution where the blind
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
 Advances in Neural Information Processing Systems 14
, 2001
"... Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher ..."
Abstract

Cited by 664 (8 self)
 Add to MetaCart
Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered.
GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems
 SIAM J. SCI. STAT. COMPUT
, 1986
"... We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered a ..."
Abstract

Cited by 2046 (40 self)
 Add to MetaCart
We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
, 1997
"... We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a highdimensional space. We take advantage of the observation that the images ..."
Abstract

Cited by 2263 (18 self)
 Add to MetaCart
of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space  if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce selfshadowing, images will deviate
Results 1  10
of
141,290