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IJCNN
2008
IEEE
15 years 7 months ago
Sparse kernel density estimator using orthogonal regression based on D-Optimality experimental design
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
Sheng Chen, Xia Hong, Chris J. Harris
121
Voted
ICASSP
2010
IEEE
15 years 24 days ago
Algorithms for robust linear regression by exploiting the connection to sparse signal recovery
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...
Yuzhe Jin, Bhaskar D. Rao
123
Voted
JMIV
2011
138views more  JMIV 2011»
14 years 7 months ago
Direct Sparse Deblurring
We propose a deblurring algorithm that explicitly takes into account the sparse characteristics of natural images and does not entail solving a numerically ill-conditioned backwar...
Yifei Lou, Andrea L. Bertozzi, Stefano Soatto
130
Voted
JMLR
2010
163views more  JMLR 2010»
14 years 7 months ago
Dense Message Passing for Sparse Principal Component Analysis
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Kevin Sharp, Magnus Rattray
102
Voted
ICCV
2007
IEEE
16 years 2 months ago
Fast Pixel/Part Selection with Sparse Eigenvectors
We extend the "Sparse LDA" algorithm of [7] with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold spe...
Bernard Moghaddam, Yair Weiss, Shai Avidan