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UTILIZING PRINCIPAL SINGULAR VECTORS FOR PARAMETER ESTIMATION OF MULTIPLE DAMPED SINUSOIDS
"... A new signal subspace approach for sinusoidal parameter estimation of multiple tones is proposed in this paper. Our main ideas are to stack the observed data into a matrix without reuse of elements and exploit the principal singular vectors of this matrix for parameter estimation. Comparing with the ..."
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A new signal subspace approach for sinusoidal parameter estimation of multiple tones is proposed in this paper. Our main ideas are to stack the observed data into a matrix without reuse of elements and exploit the principal singular vectors of this matrix for parameter estimation. Comparing
MotionBased Segmentation By Principal Singular Vector (PVS) Clustering Method
 Proceedings of ICASSP ’96
, 1996
"... Motionbased segmentation has recently drawn a lot of attentions. The task of identifying independent objects is called segmentation, which can use cues such as edge (boundary), color, texture, etc. Motionbased segmentation has a broad video application domain, for instance, video mosaic, objectba ..."
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Cited by 4 (4 self)
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based video coding, synthesized video, scene analysis, and object recognition. An approach based on Principal Singular Vectors (PSVs) of the image measurement matrix was proposed for separating independent moving objects[1]. After applying SVD (Singular Value Decomposition), feature blocks with different
UTILIZING PRINCIPAL SINGULAR VECTORS FOR TWODIMENSIONAL SINGLE FREQUENCY ESTIMATION
"... In this paper, frequency estimation of a twodimensional (2D) cisoid in the presence of additive white Gaussian noise is addressed. By utilizing the rankone property of the 2D noisefree data matrix, the frequencies are estimated in a separable manner from the principal left and right singular vecto ..."
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In this paper, frequency estimation of a twodimensional (2D) cisoid in the presence of additive white Gaussian noise is addressed. By utilizing the rankone property of the 2D noisefree data matrix, the frequencies are estimated in a separable manner from the principal left and right singular
Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array
"... The problem of azimuth and elevation directions of arrival (DOAs) estimation using a uniform rectangular array (URA) in single snapshot case is addressed in this paper. Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property, ..."
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The problem of azimuth and elevation directions of arrival (DOAs) estimation using a uniform rectangular array (URA) in single snapshot case is addressed in this paper. Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property
Probabilistic Principal Component Analysis
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
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Cited by 709 (5 self)
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation
Using Linear Algebra for Intelligent Information Retrieval
 SIAM REVIEW
, 1995
"... Currently, most approaches to retrieving textual materials from scientific databases depend on a lexical match between words in users' requests and those in or assigned to documents in a database. Because of the tremendous diversity in the words people use to describe the same document, lexical ..."
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Cited by 676 (18 self)
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by 200300 of the largest singular vectors are then matched against user queries. We call this retrieval method Latent Semantic Indexing (LSI) because the subspace represents important associative relationships between terms and documents that are not evident in individual documents. LSI is a completely
Indexing by latent semantic analysis
 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higherorder structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
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Cited by 3779 (35 self)
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. The particular technique used is singularvalue decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries
Tensor Decompositions and Applications
 SIAM REVIEW
, 2009
"... This survey provides an overview of higherorder tensor decompositions, their applications, and available software. A tensor is a multidimensional or N way array. Decompositions of higherorder tensors (i.e., N way arrays with N â¥ 3) have applications in psychometrics, chemometrics, signal proce ..."
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Cited by 723 (18 self)
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processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, etc. Two particular tensor decompositions can be considered to be higherorder extensions of the matrix singular value decompo
sition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
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Cited by 907 (36 self)
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and the eigenvalue problem. Key words. perturbation theory, random matrix, linear system, least squares, eigenvalue, eigenvector, invariant subspace, singular value AMS(MOS) subject classifications. 15A06, 15A12, 15A18, 15A52, 15A60 1. Introduction. Let A be a matrix and let F be a matrix valued function of A
On the distribution of the largest eigenvalue in principal components analysis
 ANN. STATIST
, 2001
"... Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart distribu ..."
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Cited by 422 (4 self)
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Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart
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