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Low-rank approximation

Known as: Low rank approximation 
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and… 
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Related topics

Related topics

23 relations
Biconvex optimizationBiplotData compressionFactor analysis

Broader (1)

Numerical linear algebra

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Non-negative Matrix Completion for the Enhancement of Snapshot Mosaic Multispectral Imagery
Multiand Hyperspectral Imaging (HSI) are characterized by the discrepancy between the dimensionality of hyperspectral image and… 
2015
2015
Unsupervised Hyperspectral Image Band Selection via Column Subset Selection
In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection… 
2014
2014
A Hierarchical Fast Solver for EFIE-MoM Analysis of Multiscale Structures at Very Low Frequencies
We present an EFIE fast solver that is stable down to very low frequencies, for very dense meshes and multi-scale problems. The… 
Highly Cited
2009
Highly Cited
2009
Polynomial Semantic Indexing
We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a… 
2009
2009
Efficient multi-label classification with hypergraph regularization
Many computer vision applications, such as image classification and video indexing, are usually multi-label classification… 
2007
2007
Fast Low-Rank Approximation for Covariance Matrices
Computing an efficient low-rank approximation of a given positive definite matrix is a ubiquitous task in statistical signal… 
2006
2006
Block‐row Hankel weighted low rank approximation
This paper extends the weighted low rank approximation (WLRA) approach to linearly structured matrices. In the case of Hankel… 
2006
2006
Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices
Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by… 
2006
2006
Blind Blur Estimation Using Low Rank Approximation of Cepstrum
The quality of image restoration from degraded images is highly dependent upon a reliable estimate of blur. This paper proposes a… 
2005
2005
Lower bounds on the size of selection and rank indexes
The <i>rank index problem</i> is the following: Preprocess and store a bit string <i>x</i> ∈ {0,1}<sup><i>n</i></sup> on a random… 
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