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Singular value decomposition

Known as: SVD, Singular-value decomposition, SV decomposition 
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It is the generalization of the… 
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Related topics

Related topics

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Apache SparkBidiagonalizationBiplotCollaborative filtering

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2007
Highly Cited
2007
Segmentation and tracking of multiple video objects
2006
2006
A Symmetry Preserving Singular Value Decomposition
A reduced order representation of a large data set is often realized through a principal component analysis based upon a singular… 
Highly Cited
2005
Highly Cited
2005
Eigensolutions of multiply connected membranes using the method of fundamental solutions
Highly Cited
2004
Highly Cited
2004
Frequency synchronization for generalized OFDMA uplink
In orthogonal frequency division multiple access (OFDMA), the total spectral resource is partitioned into multiple orthogonal… 
2004
2004
Hyperspectral pixel unmixing using singular value decomposition
A case study in pixel unmixing is performed using the singular value decomposition method. Using a linear mixing model, mixed… 
Highly Cited
2003
Highly Cited
2003
Ensemble Forecasts and the Properties of Flow-Dependent Analysis-Error Covariance Singular Vectors
Abstract Approximations to flow-dependent analysis-error covariance singular vectors (AEC SVs) were calculated in a dry, T31 L15… 
Highly Cited
2002
Highly Cited
2002
Wavelet-based corner detection technique using optimal scale
Highly Cited
1998
Highly Cited
1998
Second-order calibration: bilinear least squares regression and a simple alternative
1994
1994
Tomographic resolution without singular value decomposition
An explicit procedure is presented for computing both model and data resolution matrices within a Paige-Saunders LSQR algorithm… 
Highly Cited
1985
Highly Cited
1985
GEMINI: Program for analysis of structural equations with standard errors of indirect effects
vector of estimated direct effects, and E(o) is the variance/covariance matrix for the estimates in <'>. (For a complete account… 
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