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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… Expand
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Papers overview

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Review
2019
Review
2019
Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data… Expand
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Highly Cited
2010
Highly Cited
2010
  • L. Grasedyck
  • SIAM J. Matrix Anal. Appl.
  • 2010
  • Corpus ID: 30154794
We define the hierarchical singular value decomposition (SVD) for tensors of order $d\geq2$. This hierarchical SVD has properties… Expand
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Highly Cited
2010
Highly Cited
2010
The main objective of developing an image-watermarking technique is to satisfy both imperceptibility and robustness requirements… Expand
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Highly Cited
2007
Highly Cited
2007
Let A be a real m×n matrix with m≧n. It is well known (cf. [4]) that $$A = U\sum {V^T}$$ (1) where $${U^T}U = {V^T}V… Expand
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Highly Cited
2007
Highly Cited
2007
The regularized SVD model has O(NK + MK) parameters (N users, M movies, K features). We propose two models with O(MK) parameters… Expand
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Highly Cited
2004
Highly Cited
2004
We consider the problem of partitioning a set of m points in the n-dimensional Euclidean space into k clusters (usually m and n… Expand
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Highly Cited
2002
Highly Cited
2002
We introduce an incremental singular value decomposition (SVD) of incomplete data. The SVD is developed as data arrives, and can… Expand
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Highly Cited
2000
Highly Cited
2000
We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties… Expand
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Highly Cited
1998
Highly Cited
1998
We present and analyze low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems using the… Expand
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Highly Cited
1976
Highly Cited
1976
The numerical techniques of transform image coding are well known in the image bandwidth compression literature. This concise… Expand
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