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» Distributed sparse linear regression
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ICASSP
2010
IEEE
13 years 4 months 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
ICDM
2008
IEEE
128views Data Mining» more  ICDM 2008»
13 years 11 months ago
Cost-Sensitive Parsimonious Linear Regression
We examine linear regression problems where some features may only be observable at a cost (e.g., in medical domains where features may correspond to diagnostic tests that take ti...
Robby Goetschalckx, Kurt Driessens, Scott Sanner
TSP
2010
12 years 11 months ago
Estimating multiple frequency-hopping signal parameters via sparse linear regression
Abstract--Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating...
Daniele Angelosante, Georgios B. Giannakis, Nichol...
TASLP
2008
124views more  TASLP 2008»
13 years 4 months ago
Sparse Linear Regression With Structured Priors and Application to Denoising of Musical Audio
Abstract--We describe in this paper an audio denoising technique based on sparse linear regression with structured priors. The noisy signal is decomposed as a linear combination of...
Cédric Févotte, Bruno Torrésa...
JMLR
2010
152views more  JMLR 2010»
12 years 11 months ago
Bayesian Generalized Kernel Models
We propose a fully Bayesian approach for generalized kernel models (GKMs), which are extensions of generalized linear models in the feature space induced by a reproducing kernel. ...
Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. ...