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Matrix Completion with Deterministic Sampling: Theories and Methods
Guangcan Liu, Qingshan Liu, Xiao-Tong Yuan +1
In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent…
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
Guangcan Liu, Huan Xu, Shuicheng Yan
In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of m…
Closed-Form Solutions to A Category of Nuclear Norm Minimization Problems
Guangcan Liu, Ju Sun, Shuicheng Yan
It is an efficient and effective strategy to utilize the nuclear norm approximation to learn low-rank matrices, which arise frequently in machine learning and computer vision. So t…
Robust Recovery of Subspace Structures by Low-Rank Representation
Guangcan Liu, Zhouchen Lin, Shuicheng Yan +3
In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samp…