10 search results - page 1 / 2 » Minimax Rates of Estimation for Sparse PCA in High Dimension... |
JMLR
2012
11 years 9 months ago
2012
We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove o...
CORR
2011
Springer
13 years 1 months ago
2011
Springer
We analyze a class of estimators based on a convex relaxation for solving highdimensional matrix decomposition problems. The observations are the noisy realizations of the sum of ...
JMLR
2012
11 years 9 months ago
2012
Sparse additive models are families of d-variate functions with the additive decomposition f∗ = ∑j∈S f∗ j , where S is an unknown subset of cardinality s d. In this paper,...
JMLR
2012
11 years 9 months ago
2012
Often, high dimensional data lie close to a low-dimensional submanifold and it is of interest to understand the geometry of these submanifolds. The homology groups of a manifold a...
TIT
1998
13 years 6 months ago
1998
—Probability models are estimated by use of penalized log-likelihood criteria related to AIC and MDL. The accuracies of the density estimators are shown to be related to the trad...