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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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Papers overview

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2016
2016
Multiand Hyperspectral Imaging (HSI) are characterized by the discrepancy between the dimensionality of hyperspectral image and… 
2015
2015
In this paper we consider the classical problem of finding a low rank approximation of a given matrix. In a least squares sense a… 
2014
2014
Subspace segmentation is the problem of segmenting (or grouping) a set of n data points into a number of clusters, with each… 
2013
2013
Summary form only given. Block-based Discrete Cosine Transform (BDCT) has been widely used in image and video compression due to… 
Highly Cited
2009
Highly Cited
2009
We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a… 
2007
2007
Computing an efficient low-rank approximation of a given positive definite matrix is a ubiquitous task in statistical signal… 
2006
2006
This paper extends the weighted low rank approximation (WLRA) approach to linearly structured matrices. In the case of Hankel… 
2006
2006
Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by… 
2006
2006
The quality of image restoration from degraded images is highly dependent upon a reliable estimate of blur. This paper proposes a… 
2005
2005
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…