155 citations · 888 across the 24 of their papers we have counts for
4 papers · 1 filter
Robust PCA by Manifold Optimization
Teng Zhang, Yi Yang
Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations. This work considers the problem of robust PCA as a no…
Spectral Clustering Based on Local PCA
Ery Arias-Castro, Gilad Lerman, Teng Zhang
We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest nei…
A Novel M-Estimator for Robust PCA
Teng Zhang, Gilad Lerman
We study the basic problem of robust subspace recovery. That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in…
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…