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CORR
2010
Springer
208views Education» more  CORR 2010»
13 years 4 months ago
Real-time Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
Chenlu Qiu, Namrata Vaswani
SIAMIS
2011
13 years 2 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
SIAMREV
2010
174views more  SIAMREV 2010»
13 years 2 months ago
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the ...
Benjamin Recht, Maryam Fazel, Pablo A. Parrilo
FOCS
2000
IEEE
14 years 1 days ago
Opportunistic Data Structures with Applications
There is an upsurging interest in designing succinct data structures for basic searching problems (see [23] and references therein). The motivation has to be found in the exponent...
Paolo Ferragina, Giovanni Manzini
ARTCOM
2009
IEEE
13 years 5 months ago
Adaptive Encoding Algorithm for Multispectral Images
A new adaptive multispectral image compression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class ...
Deepa Sankarapandi