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math.ST2014
Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields
Martin Slawski, Matthias Hein
Consider a random vector with finite second moments. If its precision matrix is an M-matrix, then all partial correlations are non-negative. If that random vector is additionally G…
math.ST2012
Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
Martin Slawski, Matthias Hein
Least squares fitting is in general not useful for high-dimensional linear models, in which the number of predictors is of the same or even larger order of magnitude than the numbe…
math.ST2006★ 3 cited
Graph Laplacians and their convergence on random neighborhood graphs
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the subma…