Sampling and multilevel coarsening algorithms for fast matrix approximations

Shashanka Ubaru; Yousef Saad

doi:10.1002/nla.2234

Sampling and multilevel coarsening algorithms for fast matrix approximations

Numerical Linear Algebra with Applications ◽

10.1002/nla.2234 ◽

2019 ◽

Vol 26 (3) ◽

pp. e2234 ◽

Cited By ~ 2

Author(s):

Shashanka Ubaru ◽

Yousef Saad

Keyword(s):

Matrix Approximations

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Efficient Computation of Robust Weighted Low-Rank Matrix Approximations Using the L_1 Norm

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2012.116 ◽

2012 ◽

Vol 34 (9) ◽

pp. 1681-1690 ◽

Cited By ~ 40

Author(s):

A. Eriksson ◽

A. van den Hengel

Keyword(s):

Low Rank ◽

Efficient Computation ◽

Rank Matrix ◽

Low Rank Matrix ◽

Matrix Approximations

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Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint

SIAM Review ◽

10.1137/110839072 ◽

2013 ◽

Vol 55 (1) ◽

pp. 65-98 ◽

Cited By ~ 34

Author(s):

Ronny Luss ◽

Marc Teboulle

Keyword(s):

Rank One ◽

Sparsity Constraint ◽

Matrix Approximations

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C382. Matrix approximations of given rank

Journal of Statistical Computation and Simulation ◽

10.1080/00949659108811351 ◽

1991 ◽

Vol 39 (3) ◽

pp. 187-191 ◽

Cited By ~ 1

Author(s):

D.R. Jensen

Keyword(s):

Matrix Approximations

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Designs for generalized linear models with random block effects via information matrix approximations

Biometrika ◽

10.1093/biomet/asv005 ◽

2015 ◽

Vol 102 (3) ◽

pp. 677-693 ◽

Cited By ~ 9

Author(s):

T. W. Waite ◽

D. C. Woods

Keyword(s):

Generalized Linear Models ◽

Linear Models ◽

Information Matrix ◽

Random Block ◽

Matrix Approximations

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Abstract 355: The integrated analysis of multiple, high-dimensional data types by joint matrix approximations of rank-1 with applications to liver cancer and glioblastoma

10.1158/1538-7445.am2014-355 ◽

2014 ◽

Author(s):

Gordon S. Okimoto

Keyword(s):

Liver Cancer ◽

High Dimensional Data ◽

High Dimensional ◽

Integrated Analysis ◽

Data Types ◽

Matrix Approximations

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Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

BioData Mining ◽

10.1186/s13040-016-0103-7 ◽

2016 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Gordon Okimoto ◽

Ashkan Zeinalzadeh ◽

Tom Wenska ◽

Michael Loomis ◽

James B. Nation ◽

...

Keyword(s):

Liver Cancer ◽

Sparse Matrix ◽

High Dimensional Data ◽

Joint Analysis ◽

High Dimensional ◽

Data Types ◽

Matrix Approximations

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Erratum: Coherent-potential and averaget-matrix approximations for disordered muffin-tin alloys. II. Application to realistic systems

Physical Review B ◽

10.1103/physrevb.23.3107 ◽

1981 ◽

Vol 23 (6) ◽

pp. 3107-3107 ◽

Cited By ~ 2

Author(s):

A. Bansil

Keyword(s):

Tin Alloys ◽

Matrix Approximations

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Coherent-potential and averaget-matrix approximations for disordered muffin-tin alloys. II. Application to realistic systems

Physical Review B ◽

10.1103/physrevb.20.4035 ◽

1979 ◽

Vol 20 (10) ◽

pp. 4035-4043 ◽

Cited By ~ 61

Author(s):

A. Bansil

Keyword(s):

Tin Alloys ◽

Matrix Approximations

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An Efficient Gauss--Newton Algorithm for Symmetric Low-Rank Product Matrix Approximations

SIAM Journal on Optimization ◽

10.1137/140971464 ◽

2015 ◽

Vol 25 (3) ◽

pp. 1571-1608 ◽

Cited By ~ 12

Author(s):

Xin Liu ◽

Zaiwen Wen ◽

Yin Zhang

Keyword(s):

Low Rank ◽

Rank Product ◽

Newton Algorithm ◽

Product Matrix ◽

Matrix Approximations

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Computing optimal low-rank matrix approximations for image processing

2013 Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2013.6810366 ◽

2013 ◽

Author(s):

Julianne Chung ◽

Matthias Chung

Keyword(s):

Image Processing ◽

Low Rank ◽

Rank Matrix ◽

Low Rank Matrix ◽

Matrix Approximations

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