Generalizing the column–row matrix decomposition to multi-way arrays

[1]  W. Marsden I and J , 2012 .

[2]  S. Goreinov,et al.  How to find a good submatrix , 2010 .

[3]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[4]  Eugene E. Tyrtyshnikov,et al.  Breaking the Curse of Dimensionality, Or How to Use SVD in Many Dimensions , 2009, SIAM J. Sci. Comput..

[5]  Petros Drineas,et al.  CUR matrix decompositions for improved data analysis , 2009, Proceedings of the National Academy of Sciences.

[6]  Eugene E. Tyrtyshnikov,et al.  Tucker Dimensionality Reduction of Three-Dimensional Arrays in Linear Time , 2008, SIAM J. Matrix Anal. Appl..

[7]  S. Goreinov,et al.  On cross approximation of multi-index arrays , 2008 .

[8]  S. Muthukrishnan,et al.  Relative-Error CUR Matrix Decompositions , 2007, SIAM J. Matrix Anal. Appl..

[9]  Jimeng Sun,et al.  Less is More: Compact Matrix Decomposition for Large Sparse Graphs , 2007, SDM.

[10]  E. Tyrtyshnikov Low-Rank Structures and Tensor Approximations for Huge-Size Data Sets , 2007 .

[11]  Petros Drineas,et al.  Tensor-CUR decompositions for tensor-based data , 2006, KDD '06.

[12]  Tamara G. Kolda,et al.  MATLAB Tensor Toolbox , 2006 .

[13]  Petros Drineas,et al.  Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition , 2006, SIAM J. Comput..

[14]  Judith M. Ford,et al.  Combining Kronecker Product Approximation with Discrete Wavelet Transforms to Solve Dense, Function-Related Linear Systems , 2003, SIAM J. Sci. Comput..

[15]  S. Goreinov,et al.  The maximum-volume concept in approximation by low-rank matrices , 2001 .

[16]  Eugene E. Tyrtyshnikov,et al.  Incomplete Cross Approximation in the Mosaic-Skeleton Method , 2000, Computing.

[17]  Joos Vandewalle,et al.  On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors , 2000, SIAM J. Matrix Anal. Appl..

[18]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[19]  G. W. Stewart,et al.  Four algorithms for the the efficient computation of truncated pivoted QR approximations to a sparse matrix , 1999, Numerische Mathematik.

[20]  S. Goreinov,et al.  Pseudo-skeleton approximations by matrices of maximal volume , 1997 .

[21]  S. Goreinov,et al.  A Theory of Pseudoskeleton Approximations , 1997 .

[22]  John J. Bartholdi,et al.  A good submatrix is hard to find , 1982, Oper. Res. Lett..

[23]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[24]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[25]  Peter Lancaster,et al.  The theory of matrices , 1969 .

[26]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[27]  C. Harris Problems in measuring change , 1965 .

[28]  Severnyi Kavkaz Pseudo-Skeleton Approximations by Matrices of Maximal Volume , 2022 .