155 citations · 1.6k across the 76 of their papers we have counts for
6 papers · 1 filter
lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers
Gilad Lerman, Teng Zhang
We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spher…
Hybrid Linear Modeling via Local Best-fit Flats
Teng Zhang, Arthur Szlam, Yi Wang +1
We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e.,…
Least squares approximations of measures via geometric condition numbers
Gilad Lerman, J. Tyler Whitehouse
For a probability measure on a real separable Hilbert space, we are interested in "volume-based" approximations of the d-dimensional least squares error of it, i.e., least squares…
Randomized hybrid linear modeling by local best-fit flats
Teng Zhang, Arthur Szlam, Yi Wang +1
The hybrid linear modeling problem is to identify a set of d-dimensional affine sets in a D-dimensional Euclidean space. It arises, for example, in object tracking and structure fr…
Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric lp Minimization
Gilad Lerman, Teng Zhang
We assume data independently sampled from a mixture distribution on the unit ball of the D-dimensional Euclidean space with K+1 components: the first component is a uniform distrib…
Spectral clustering based on local linear approximations
Ery Arias-Castro, Guangliang Chen, Gilad Lerman
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface; the sample may be…