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Spectrally Optimal Factorization of Incomplete Matrices Pedro M. Q. Aguiar Marko Stosic
 

Summary: Spectrally Optimal Factorization of Incomplete Matrices
Pedro M. Q. Aguiar Marko Stosic
Jo~ao M. F. Xavier
Institute for Systems and Robotics / IST, Lisboa, Portugal
aguiar@isr.ist.utl.pt
Abstract
From the recovery of structure from motion to the sep-
aration of style and content, many problems in computer
vision have been successfully approached by using bilinear
models. The reason for the success of these models is that
a globally optimal decomposition is easily obtained from
the Singular Value Decomposition (SVD) of the observa-
tion matrix. However, in practice, the observation matrix is
often incomplete, the SVD can not be used, and only sub-
optimal solutions are available. The majority of these so-
lutions are based on iterative local refinements of a given
cost function, and lack any guarantee of convergence to the
global optimum. In this paper, we propose a globally opti-
mal solution, for particular patterns of missing entries. To
achieve this goal, we re-formulate the problem as the mini-

  

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

 

Collections: Engineering