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» Structured Low Rank Approximation of a Bezout Matrix
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SIAMJO
2011
13 years 5 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»
13 years 10 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»
13 years 10 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»
13 years 11 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
CORR
2006
Springer
178views Education» more  CORR 2006»
13 years 11 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,...