59 search results - page 3 / 12 » Robust regression using sparse learning for high dimensional... |
JMLR
2002
13 years 7 months ago
2002
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
NECO
2002
13 years 7 months ago
2002
In the presence of a heavy-tail noise distribution, regression becomes much more di cult. Traditional robust regression methods assume that the noise distribution is symmetric and...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...