59 search results - page 1 / 12 » Robust regression using sparse learning for high dimensional... |
ICASSP
2010
IEEE
13 years 5 months ago
2010
IEEE
Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the comb...
CVPR
2010
IEEE
13 years 5 months ago
2010
IEEE
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...
JMLR
2010
12 years 11 months ago
2010
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
JMLR
2012
11 years 7 months ago
2012
We consider the multiple-response regression problem, where the response is subject to sparse gross errors, in the high-dimensional setup. We propose a tractable regularized M-est...
TSP
2010
12 years 11 months ago
2010
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...