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SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
9 years 9 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
TSP
2008
178views more  TSP 2008»
9 years 2 months ago
Heteroscedastic Low-Rank Matrix Approximation by the Wiberg Algorithm
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...
Pei Chen
ACCV
2010
Springer
8 years 9 months ago
Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery
We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recover...
Lun Wu, Arvind Ganesh, Boxin Shi, Yasuyuki Matsush...
AAAI
2012
7 years 4 months ago
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...
Tomer Levinboim, Fei Sha
SIAMMAX
2010
164views more  SIAMMAX 2010»
8 years 9 months ago
Uniqueness of Low-Rank Matrix Completion by Rigidity Theory
The problem of completing a low-rank matrix from a subset of its entries is often encountered in the analysis of incomplete data sets exhibiting an underlying factor model with app...
Amit Singer, Mihai Cucuringu
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