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SADM
2008
178views more  SADM 2008»
13 years 6 months ago
Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem
: Nonnegative matrix approximation (NNMA) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields with applications ranging from...
Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
CVPR
2009
IEEE
15 years 2 months ago
StaRSaC: Stable Random Sample Consensus for Parameter Estimation
We address the problem of parameter estimation in presence of both uncertainty and outlier noise. This is a common occurrence in computer vision: feature localization is perform...
Jongmoo Choi, Gérard G. Medioni
ICIP
2010
IEEE
13 years 5 months ago
Constructing a sparse convolution matrix for shift varying image restoration problems
Convolution operator is a linear operator characterized by a point spread functions (PSF). In classical image restoration problems, the blur is usually shift invariant and so the ...
Stanley H. Chan
ICCD
2007
IEEE
121views Hardware» more  ICCD 2007»
14 years 4 months ago
Fast power network analysis with multiple clock domains
This paper proposes an efficient analysis flow and an algorithm to identify the worst case noise for power networks with multiple clock domains. First, we apply the Laplace transf...
Wanping Zhang, Ling Zhang, Rui Shi, He Peng, Zhi Z...
ICASSP
2009
IEEE
13 years 5 months ago
Weighted nonnegative matrix factorization
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity...
Yong-Deok Kim, Seungjin Choi