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» Structured Low Rank Approximation of a Bezout Matrix
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SIAMJO
2011
14 years 4 months ago
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
Min Tao, Xiaoming Yuan
SIAMSC
2008
167views more  SIAMSC 2008»
14 years 9 months ago
Low-Dimensional Polytope Approximation and Its Applications to Nonnegative Matrix Factorization
In this study, nonnegative matrix factorization is recast as the problem of approximating a polytope on the probability simplex by another polytope with fewer facets. Working on th...
Moody T. Chu, Matthew M. Lin
PRL
2006
117views more  PRL 2006»
14 years 9 months ago
Non-iterative generalized low rank approximation of matrices
: As an extension to 2DPCA, Generalized Low Rank Approximation of Matrices (GLRAM) applies two-sided (i.e., the left and right) rather than single-sided (i.e., the left or the righ...
Jun Liu, Songcan Chen
AUTOMATICA
2008
139views more  AUTOMATICA 2008»
14 years 9 months ago
Structured low-rank approximation and its applications
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equival...
Ivan Markovsky
65
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CORR
2006
Springer
178views Education» more  CORR 2006»
14 years 9 months ago
Low-rank matrix factorization with attributes
We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classi...
Jacob Abernethy, Francis Bach, Theodoros Evgeniou,...