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Found 4 papers, 0 papers with code
Sparsistency and agnostic inference in sparse PCA
What can be said about the results of sparse PCA without assuming a sparse and unique truth?
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-$d$ projection matrices (the Fantope).
Minimax sparse principal subspace estimation in high dimensions
We study sparse principal components analysis in high dimensions, where $p$ (the number of variables) can be much larger than $n$ (the number of observations), and analyze the problem of estimating the subspace spanned by the principal eigenvectors of the population covariance matrix.
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
We propose a novel hierarchical, nonlinear model that predicts brain activity in area V1 evoked by natural images.
