10 citations · 21 across the 5 of their papers we have counts for
4 papers · 1 filter
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
Qing Qu, Zhihui Zhu, Xiao Li +3
The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applicat…
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms
Zhihui Zhu, Yifan Wang, Daniel P. Robinson +3
Recent methods for learning a linear subspace from data corrupted by outliers are based on convex and nuclear norm optimization and require the dimension of the subspace a…
An algebraic-geometric approach for linear regression without correspondences
Manolis C. Tsakiris, Liangzu Peng, Aldo Conca +3
Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the ob…
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Manolis C. Tsakiris, Rene Vidal
Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to an unknown union of low-dimensional linear subspaces; a proble…