Results 1  10
of
5,236
On variations of power iteration
 In: Proc. Int’l Conf. Artificial Neural Networks. Volume 2
, 2005
"... Abstract. The power iteration is a classical method for computing the eigenvector associated with the largest eigenvalue of a matrix. The subspace iteration is an extension of the power iteration where the subspace spanned by n largest eigenvectors of a matrix, is determined. The natural power iter ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Abstract. The power iteration is a classical method for computing the eigenvector associated with the largest eigenvalue of a matrix. The subspace iteration is an extension of the power iteration where the subspace spanned by n largest eigenvectors of a matrix, is determined. The natural power
Power Iteration Clustering
"... We show that the power iteration, typically used to approximate the dominant eigenvector of a matrix, can be applied to a normalized affinity matrix to create a onedimensional embedding of the underlying data. This embedding is then used, as in spectral clustering, to cluster the data via kmeans. ..."
Abstract

Cited by 38 (5 self)
 Add to MetaCart
We show that the power iteration, typically used to approximate the dominant eigenvector of a matrix, can be applied to a normalized affinity matrix to create a onedimensional embedding of the underlying data. This embedding is then used, as in spectral clustering, to cluster the data via k
"GrabCut”  interactive foreground extraction using iterated graph cuts
 ACM TRANS. GRAPH
, 2004
"... The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently ..."
Abstract

Cited by 1130 (36 self)
 Add to MetaCart
. Recently, an approach based on optimization by graphcut has been developed which successfully combines both types of information. In this paper we extend the graphcut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power
A Framework for Uplink Power Control in Cellular Radio Systems
 IEEE Journal on Selected Areas in Communications
, 1996
"... In cellular wireless communication systems, transmitted power is regulated to provide each user an acceptable connection by limiting the interference caused by other users. Several models have been considered including: (1) fixed base station assignment where the assignment of users to base stations ..."
Abstract

Cited by 651 (18 self)
 Add to MetaCart
stations is fixed, (2) minimum power assignment where a user is iteratively assigned to the base station at which its signal to interference ratio is highest, and (3) diversity reception, where a user's signal is combined from several or perhaps all base stations. For the above models, the uplink
1Fast Approximated Power Iteration Subspace Tracking
"... Abstract — This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm, referred to as the fast API method, guarantees the orthonormality of the subspace weigh ..."
Abstract
 Add to MetaCart
Abstract — This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm, referred to as the fast API method, guarantees the orthonormality of the subspace
Normalized power iterations for the computation of SVD
"... 1 A randomized algorithm for low rank matrix aproximation We are interested in finding an approximation UΣV T to the m × n matrix A, where U is an m × k orthogonal matrix, Σ is a k × k diagonal matrix, V is an n × k orthognal matrix, and k is a user set positive integer less than m; usually k ≪ m. R ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
1 A randomized algorithm for low rank matrix aproximation We are interested in finding an approximation UΣV T to the m × n matrix A, where U is an m × k orthogonal matrix, Σ is a k × k diagonal matrix, V is an n × k orthognal matrix, and k is a user set positive integer less than m; usually k ≪ m. Recently there have been several papers which have approached this problem by determining the approximate range space of A by applying it to a set of random vectors. The full SVD is then computed on A’s approximate range, giving algorithms that run in O(mkn) time; see [5, 3, 6]. The purpose of this short note is to discuss a particular variant of these algorithms which is a good choice when a high quality (as measured by operator norm) low rank approximation of a matrix is desired, and memory is a limiting quantity. The algorithm takes as input nonnegative integers q and l such that m> l> k (usually l is slightly bigger than k, say k + 10), and then goes as follows:
APPROXIMATED POWER ITERATIONS FOR FAST SUBSPACE TRACKING
"... This paper introduces a fast implementation of the power iterations method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm guarantees the orthonormality of the estimated subspace weighting matrix at each iteration, and sa ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
This paper introduces a fast implementation of the power iterations method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm guarantees the orthonormality of the estimated subspace weighting matrix at each iteration
Sum power iterative waterfilling for multiantenna Gaussian broadcast channels
 IEEE Trans. Inform. Theory
, 2005
"... In this paper we consider the problem of maximizing sum rate of a multipleantenna Gaussian broadcast channel. It was recently found that dirty paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e. the optimal transmit covariance str ..."
Abstract

Cited by 136 (14 self)
 Add to MetaCart
structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a wellstructured convex multipleaccess channel
Unsupervised word sense disambiguation rivaling supervised methods
 IN PROCEEDINGS OF THE 33RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
, 1995
"... This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require timeconsuming hand annotations. The algorithm is based on two powerful constraints  that words tend to have ..."
Abstract

Cited by 638 (4 self)
 Add to MetaCart
This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require timeconsuming hand annotations. The algorithm is based on two powerful constraints  that words tend to have
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
, 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
Abstract

Cited by 723 (8 self)
 Add to MetaCart
differences t test based on 10fold crossvalidation, exhibits somewhat elevated probability of type I error. A fourth test, McNemar’s test, is shown to have low type I error. The fifth test is a new test, 5 × 2 cv, based on five iterations of twofold crossvalidation. Experiments show that this test also has
Results 1  10
of
5,236