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» Introduction to Randomized Algorithms
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JMLR
2010
104views more  JMLR 2010»
14 years 11 months ago
Increasing Feature Selection Accuracy for L1 Regularized Linear Models
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
Abhishek Jaiantilal, Gregory Z. Grudic
TIP
2010
162views more  TIP 2010»
14 years 11 months ago
Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty
Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness ...
A. K. Qin, David A. Clausi
TIP
2010
129views more  TIP 2010»
14 years 11 months ago
Image Segmentation by MAP-ML Estimations
Abstract--Image segmentation plays an important role in computer vision and image analysis. In this paper, image segmentation is formulated as a labeling problem under a probabilit...
Shifeng Chen, Liangliang Cao, Yueming Wang, Jianzh...
ICASSP
2011
IEEE
14 years 8 months ago
A clustering based framework for dictionary block structure identification
Sparse representations over redundant dictionaries offer an efficient paradigm for signal representation. Recently block-sparsity has been put forward as a prior condition for so...
Ender M. Eksioglu
CVPR
2009
IEEE
1468views Computer Vision» more  CVPR 2009»
17 years 9 days ago
Hardware-Efficient Belief Propagation
Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation beca...
Chao-Chung Cheng, Chia-Kai Liang, Homer H. Chen, L...