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» Computing the Dimension of Linear Subspaces
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130
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ISCAS
2003
IEEE
131views Hardware» more  ISCAS 2003»
15 years 9 months ago
Process variation dimension reduction based on SVD
We propose an algorithm based on singular value decomposition (SVD) to reduce the number of process variation variables. With few process variation variables, fault simulation and...
Zhuo Li, Xiang Lu, Weiping Shi
124
Voted
TIT
2008
73views more  TIT 2008»
15 years 3 months ago
L-CAMP: Extremely Local High-Performance Wavelet Representations in High Spatial Dimension
A new wavelet-based methodology for representing data on regular grids is introduced and studied. The main attraction of this new L-CAMP methodology is in the way it scales with th...
Youngmi Hur, Amos Ron
123
Voted
IEICET
2008
95views more  IEICET 2008»
15 years 3 months ago
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing the BLUE usually requires the ...
Masashi Sugiyama, Motoaki Kawanabe, Gilles Blancha...
125
Voted
GD
2005
Springer
15 years 9 months ago
Drawing Clustered Graphs in Three Dimensions
Clustered graph is a very useful model for drawing large and complex networks. This paper presents a new method for drawing clustered graphs in three dimensions. The method uses a ...
Joshua Wing Kei Ho, Seok-Hee Hong
141
Voted
ICCV
2011
IEEE
14 years 3 months ago
Domain Adaptation for Object Recognition: An Unsupervised Approach
Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we p...
Raghuraman Gopalan, Ruonan Li, Rama Chellappa